D4ve-R
commented
9 months ago @JoshuaFurman, try running it with this model:
your clip model file seems to be in the wrong encoding and too small in comparison to other clip models
Open JoshuaFurman opened 9 months ago
D4ve-R
commented
9 months ago @JoshuaFurman, try running it with this model:
your clip model file seems to be in the wrong encoding and too small in comparison to other clip models
JoshuaFurman
commented
9 months ago Thanks for the suggestion @D4ve-R. Just downloaded those models and I'm seeing the same errors:
objc[84786]: Class GGMLMetalClass is implemented in both /Users/furm/projects/watcher/venv/lib/python3.9/site-packages/llama_cpp/libllama.dylib (0x1009a8228) and /Users/furm/projects/watcher/venv/lib/python3.9/site-packages/llama_cpp/libllava.dylib (0x10267c228). One of the two will be used. Which one is undefined.
Exception ignored in: <function Llava15ChatHandler.__del__ at 0x1012adf70>
Traceback (most recent call last):
File "/Users/furm/projects/watcher/venv/lib/python3.9/site-packages/llama_cpp/llama_chat_format.py", line 996, in __del__
File "/Users/furm/projects/watcher/venv/lib/python3.9/site-packages/llama_cpp/_utils.py", line 20, in __enter__
LookupError: unknown encoding: ascii
Exception ignored in: <function _LlamaModel.__del__ at 0x102413d30>
Traceback (most recent call last):
File "/Users/furm/projects/watcher/venv/lib/python3.9/site-packages/llama_cpp/llama.py", line 240, in __del__
File "/Users/furm/projects/watcher/venv/lib/python3.9/site-packages/llama_cpp/_utils.py", line 20, in __enter__
LookupError: unknown encoding: ascii
Exception ignored in: <function _LlamaContext.__del__ at 0x102417ca0>
Traceback (most recent call last):
File "/Users/furm/projects/watcher/venv/lib/python3.9/site-packages/llama_cpp/llama.py", line 422, in __del__
File "/Users/furm/projects/watcher/venv/lib/python3.9/site-packages/llama_cpp/_utils.py", line 20, in __enter__
LookupError: unknown encoding: ascii
Exception ignored in: <function _LlamaBatch.__del__ at 0x10241c040>
Traceback (most recent call last):
File "/Users/furm/projects/watcher/venv/lib/python3.9/site-packages/llama_cpp/llama.py", line 670, in __del__
File "/Users/furm/projects/watcher/venv/lib/python3.9/site-packages/llama_cpp/_utils.py", line 20, in __enter__
LookupError: unknown encoding: ascii
clip_model_load: total allocated memory: 195.95 MB
encode_image_with_clip: image encoded in 1928.89 ms by CLIP ( 3.35 ms per image patch)Possibly my implementation may be wrong?
from llama_cpp import Llama
from llama_cpp.llama_chat_format import Llava15ChatHandler
import base64
def image_to_base64_data_uri(file_path):
with open(file_path, "rb") as img_file:
base64_data = base64.b64encode(img_file.read()).decode('utf-8')
return f"data:image/png;base64,{base64_data}"
file_path = '/Users/furm/projects/llama.cpp/llava-stuff/selfie.png'
data_uri = image_to_base64_data_uri(file_path)
### Test
clip_model_path = "models/mmproj-model-f16.gguf"
model_path = "models/ggml-model-q4_k.gguf"
chat_handler = Llava15ChatHandler(clip_model_path=clip_model_path)
llm = Llama(
model_path = model_path,
chat_format = "llava-1-5",
chat_handler = chat_handler,
n_ctx = 2048, # n_ctx should be increased to accomodate the image embedding
n_gpu_layers = 1,
logits_all = True,
verbose = False
)
llm.create_chat_completion(
messages=[
{"role": "system", "content": "You are an assistant who perfectly describes images."},
{
"role": "user",
"content": [
{"type": "image_url", "image_url": {"url": data_uri}},
{"type": "text", "text": "Describe this image in detail please."}
]
}
]
)Funny enough I just tested a simple inference with openhermes-2.5-mistral7b and the inference completes but I'm still being thrown the LookupError: unknown encoding: ascii...
Code:
from llama_cpp import Llama
llm = Llama(model_path="./models/openhermes/openhermes-2.5-mistral-7b.Q5_K_M.gguf", n_gpu_layers=1, verbose = False)
output = llm(
"Name the planets in the solar system? ", # Prompt
echo = True # Echo the prompt back in the output
)
print(output)What happens when you run with n_gpu_layers = 0?
tk-master
commented
9 months ago I tested the code and I got no errors on windows, seems like an issue on mac
D4ve-R
commented
9 months ago It works fine on a 2018 intel MacBook Pro Ubuntu 22.04 cpu/nv gpu works too
JoshuaFurman
commented
9 months ago Interesting... Must be an issue with Apple Silicon. Works just fine with llama.cpp directly.
I've tried with both n_gpu_layers = 1 and n_gpu_layers = 0 to force CPU but no luck.
Appreciate the help though.
abetlen
commented
9 months ago Hey @JoshuaFurman not sure the cause of this issue but 2 things that stand out:
objc[84786]: Class GGMLMetalClass is implemented in both /Users/furm/projects/watcher/venv/lib/python3.9/site-packages/llama_cpp/libllama.dylib (0x1009a8228) and /Users/furm/projects/watcher/venv/lib/python3.9/site-packages/llama_cpp/libllava.dylib (0x10267c228). One of the two will be used. Which one is undefined. this implies some issue with how we're building both llama and llava as shared libraries.Traceback (most recent call last): File "/Users/furm/projects/watcher/venv/lib/python3.9/site-packages/llama_cpp/llama_chat_format.py", line 996, in __del__ File "/Users/furm/projects/watcher/venv/lib/python3.9/site-packages/llama_cpp/_utils.py", line 20, in __enter__ LookupError: unknown encoding: ascii this is an error with the stdout / stderr capture, can you rerun with Llama(.., verbose=True)?JoshuaFurman
commented
9 months ago Hey @abetlen, I added verbose=True to Llama():
Code:
from llama_cpp import Llama
from llama_cpp.llama_chat_format import Llava15ChatHandler
import base64
def image_to_base64_data_uri(file_path):
with open(file_path, "rb") as img_file:
base64_data = base64.b64encode(img_file.read()).decode('utf-8')
return f"data:image/png;base64,{base64_data}"
file_path = '/Users/furm/projects/llama.cpp/llava-stuff/selfie.png'
data_uri = image_to_base64_data_uri(file_path)
### Test
clip_model_path = "models/mmproj-model-f16.gguf"
model_path = "models/ggml-model-q4_k.gguf"
chat_handler = Llava15ChatHandler(clip_model_path=clip_model_path)
llm = Llama(
model_path = model_path,
chat_format = "llava-1-5",
chat_handler = chat_handler,
n_ctx = 2048, # n_ctx should be increased to accomodate the image embedding
n_gpu_layers = 1,
logits_all = True,
verbose = True
)
llm.create_chat_completion(
messages=[
{"role": "system", "content": "You are an assistant who perfectly describes images."},
{
"role": "user",
"content": [
{"type": "image_url", "image_url": {"url": data_uri}},
{"type": "text", "text": "Describe this image in detail please."}
]
}
]
)Resulting output:
objc[97367]: Class GGMLMetalClass is implemented in both /Users/furm/projects/watcher/venv/lib/python3.9/site-packages/llama_cpp/libllama.dylib (0x104f8c228) and /Users/furm/projects/watcher/venv/lib/python3.9/site-packages/llama_cpp/libllava.dylib (0x106a60228). One of the two will be used. Which one is undefined.
llama_model_loader: loaded meta data with 19 key-value pairs and 291 tensors from models/ggml-model-q4_k.gguf (version GGUF V2)
llama_model_loader: - tensor 0: token_embd.weight q4_K [ 4096, 32000, 1, 1 ]
llama_model_loader: - tensor 1: blk.0.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 2: blk.0.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 3: blk.0.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 4: blk.0.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 5: blk.0.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 6: blk.0.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 7: blk.0.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 8: blk.0.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 9: blk.0.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 10: blk.1.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 11: blk.1.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 12: blk.1.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 13: blk.1.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 14: blk.1.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 15: blk.1.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 16: blk.1.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 17: blk.1.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 18: blk.1.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 19: blk.2.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 20: blk.2.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 21: blk.2.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 22: blk.2.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 23: blk.2.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 24: blk.2.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 25: blk.2.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 26: blk.2.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 27: blk.2.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 28: blk.3.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 29: blk.3.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 30: blk.3.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 31: blk.3.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 32: blk.3.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 33: blk.3.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 34: blk.3.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 35: blk.3.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 36: blk.3.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 37: blk.4.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 38: blk.4.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 39: blk.4.attn_v.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 40: blk.4.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 41: blk.4.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 42: blk.4.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 43: blk.4.ffn_down.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 44: blk.4.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 45: blk.4.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 46: blk.5.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 47: blk.5.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 48: blk.5.attn_v.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 49: blk.5.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 50: blk.5.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 51: blk.5.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 52: blk.5.ffn_down.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 53: blk.5.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 54: blk.5.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 55: blk.6.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 56: blk.6.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 57: blk.6.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 58: blk.6.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 59: blk.6.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 60: blk.6.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 61: blk.6.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 62: blk.6.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 63: blk.6.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 64: blk.7.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 65: blk.7.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 66: blk.7.attn_v.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 67: blk.7.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 68: blk.7.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 69: blk.7.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 70: blk.7.ffn_down.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 71: blk.7.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 72: blk.7.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 73: blk.8.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 74: blk.8.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 75: blk.8.attn_v.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 76: blk.8.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 77: blk.8.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 78: blk.8.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 79: blk.8.ffn_down.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 80: blk.8.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 81: blk.8.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 82: blk.9.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 83: blk.9.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 84: blk.9.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 85: blk.9.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 86: blk.9.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 87: blk.9.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 88: blk.9.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 89: blk.9.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 90: blk.9.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 91: blk.10.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 92: blk.10.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 93: blk.10.attn_v.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 94: blk.10.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 95: blk.10.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 96: blk.10.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 97: blk.10.ffn_down.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 98: blk.10.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 99: blk.10.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 100: blk.11.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 101: blk.11.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 102: blk.11.attn_v.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 103: blk.11.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 104: blk.11.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 105: blk.11.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 106: blk.11.ffn_down.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 107: blk.11.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 108: blk.11.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 109: blk.12.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 110: blk.12.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 111: blk.12.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 112: blk.12.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 113: blk.12.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 114: blk.12.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 115: blk.12.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 116: blk.12.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 117: blk.12.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 118: blk.13.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 119: blk.13.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 120: blk.13.attn_v.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 121: blk.13.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 122: blk.13.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 123: blk.13.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 124: blk.13.ffn_down.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 125: blk.13.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 126: blk.13.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 127: blk.14.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 128: blk.14.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 129: blk.14.attn_v.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 130: blk.14.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 131: blk.14.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 132: blk.14.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 133: blk.14.ffn_down.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 134: blk.14.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 135: blk.14.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 136: blk.15.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 137: blk.15.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 138: blk.15.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 139: blk.15.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 140: blk.15.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 141: blk.15.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 142: blk.15.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 143: blk.15.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 144: blk.15.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 145: blk.16.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 146: blk.16.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 147: blk.16.attn_v.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 148: blk.16.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 149: blk.16.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 150: blk.16.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 151: blk.16.ffn_down.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 152: blk.16.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 153: blk.16.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 154: blk.17.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 155: blk.17.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 156: blk.17.attn_v.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 157: blk.17.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 158: blk.17.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 159: blk.17.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 160: blk.17.ffn_down.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 161: blk.17.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 162: blk.17.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 163: blk.18.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 164: blk.18.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 165: blk.18.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 166: blk.18.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 167: blk.18.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 168: blk.18.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 169: blk.18.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 170: blk.18.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 171: blk.18.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 172: blk.19.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 173: blk.19.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 174: blk.19.attn_v.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 175: blk.19.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 176: blk.19.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 177: blk.19.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 178: blk.19.ffn_down.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 179: blk.19.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 180: blk.19.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 181: blk.20.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 182: blk.20.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 183: blk.20.attn_v.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 184: blk.20.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 185: blk.20.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 186: blk.20.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 187: blk.20.ffn_down.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 188: blk.20.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 189: blk.20.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 190: blk.21.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 191: blk.21.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 192: blk.21.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 193: blk.21.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 194: blk.21.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 195: blk.21.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 196: blk.21.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 197: blk.21.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 198: blk.21.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 199: blk.22.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 200: blk.22.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 201: blk.22.attn_v.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 202: blk.22.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 203: blk.22.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 204: blk.22.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 205: blk.22.ffn_down.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 206: blk.22.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 207: blk.22.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 208: blk.23.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 209: blk.23.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 210: blk.23.attn_v.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 211: blk.23.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 212: blk.23.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 213: blk.23.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 214: blk.23.ffn_down.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 215: blk.23.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 216: blk.23.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 217: blk.24.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 218: blk.24.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 219: blk.24.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 220: blk.24.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 221: blk.24.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 222: blk.24.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 223: blk.24.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 224: blk.24.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 225: blk.24.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 226: blk.25.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 227: blk.25.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 228: blk.25.attn_v.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 229: blk.25.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 230: blk.25.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 231: blk.25.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 232: blk.25.ffn_down.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 233: blk.25.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 234: blk.25.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 235: blk.26.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 236: blk.26.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 237: blk.26.attn_v.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 238: blk.26.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 239: blk.26.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 240: blk.26.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 241: blk.26.ffn_down.weight q4_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 242: blk.26.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 243: blk.26.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 244: blk.27.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 245: blk.27.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 246: blk.27.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 247: blk.27.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 248: blk.27.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 249: blk.27.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 250: blk.27.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 251: blk.27.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 252: blk.27.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 253: blk.28.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 254: blk.28.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 255: blk.28.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 256: blk.28.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 257: blk.28.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 258: blk.28.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 259: blk.28.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 260: blk.28.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 261: blk.28.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 262: blk.29.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 263: blk.29.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 264: blk.29.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 265: blk.29.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 266: blk.29.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 267: blk.29.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 268: blk.29.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 269: blk.29.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 270: blk.29.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 271: blk.30.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 272: blk.30.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 273: blk.30.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 274: blk.30.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 275: blk.30.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 276: blk.30.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 277: blk.30.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 278: blk.30.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 279: blk.30.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 280: blk.31.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 281: blk.31.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 282: blk.31.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]
llama_model_loader: - tensor 283: blk.31.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 284: blk.31.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 285: blk.31.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]
llama_model_loader: - tensor 286: blk.31.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]
llama_model_loader: - tensor 287: blk.31.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 288: blk.31.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 289: output_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 290: output.weight q6_K [ 4096, 32000, 1, 1 ]
llama_model_loader: - kv 0: general.architecture str = llama
llama_model_loader: - kv 1: general.name str = ..
llama_model_loader: - kv 2: llama.context_length u32 = 32768
llama_model_loader: - kv 3: llama.embedding_length u32 = 4096
llama_model_loader: - kv 4: llama.block_count u32 = 32
llama_model_loader: - kv 5: llama.feed_forward_length u32 = 14336
llama_model_loader: - kv 6: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 7: llama.attention.head_count u32 = 32
llama_model_loader: - kv 8: llama.attention.head_count_kv u32 = 8
llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 10: llama.rope.freq_base f32 = 10000.000000
llama_model_loader: - kv 11: general.file_type u32 = 15
llama_model_loader: - kv 12: tokenizer.ggml.model str = llama
llama_model_loader: - kv 13: tokenizer.ggml.tokens arr[str,32000] = ["<unk>", "<s>", "</s>", "<0x00>", "<...
llama_model_loader: - kv 14: tokenizer.ggml.scores arr[f32,32000] = [0.000000, 0.000000, 0.000000, 0.0000...
llama_model_loader: - kv 15: tokenizer.ggml.token_type arr[i32,32000] = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
llama_model_loader: - kv 16: tokenizer.ggml.bos_token_id u32 = 1
llama_model_loader: - kv 17: tokenizer.ggml.eos_token_id u32 = 2
llama_model_loader: - kv 18: general.quantization_version u32 = 2
llama_model_loader: - type f32: 65 tensors
llama_model_loader: - type q4_K: 193 tensors
llama_model_loader: - type q6_K: 33 tensors
llm_load_vocab: special tokens definition check successful ( 259/32000 ).
llm_load_print_meta: format = GGUF V2
llm_load_print_meta: arch = llama
llm_load_print_meta: vocab type = SPM
llm_load_print_meta: n_vocab = 32000
llm_load_print_meta: n_merges = 0
llm_load_print_meta: n_ctx_train = 32768
llm_load_print_meta: n_embd = 4096
llm_load_print_meta: n_head = 32
llm_load_print_meta: n_head_kv = 8
llm_load_print_meta: n_layer = 32
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_gqa = 4
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-05
llm_load_print_meta: f_clamp_kqv = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: n_ff = 14336
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 10000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_yarn_orig_ctx = 32768
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: model type = 7B
llm_load_print_meta: model ftype = mostly Q4_K - Medium
llm_load_print_meta: model params = 7.24 B
llm_load_print_meta: model size = 4.07 GiB (4.83 BPW)
llm_load_print_meta: general.name = ..
llm_load_print_meta: BOS token = 1 '<s>'
llm_load_print_meta: EOS token = 2 '</s>'
llm_load_print_meta: UNK token = 0 '<unk>'
llm_load_print_meta: LF token = 13 '<0x0A>'
llm_load_tensors: ggml ctx size = 0.11 MiB
llm_load_tensors: mem required = 4165.47 MiB
...............................................................................................
llama_new_context_with_model: n_ctx = 2048
llama_new_context_with_model: freq_base = 10000.0
llama_new_context_with_model: freq_scale = 1
llama_new_context_with_model: kv self size = 256.00 MiB
llama_build_graph: non-view tensors processed: 740/740
ggml_metal_init: allocating
ggml_metal_init: found device: Apple M2
ggml_metal_init: picking default device: Apple M2
ggml_metal_init: default.metallib not found, loading from source
ggml_metal_init: loading '/Users/furm/projects/watcher/venv/lib/python3.9/site-packages/llama_cpp/ggml-metal.metal'
ggml_metal_init: GPU name: Apple M2
ggml_metal_init: GPU family: MTLGPUFamilyApple8 (1008)
ggml_metal_init: hasUnifiedMemory = true
ggml_metal_init: recommendedMaxWorkingSetSize = 10922.67 MiB
ggml_metal_init: maxTransferRate = built-in GPU
llama_new_context_with_model: compute buffer total size = 159.07 MiB
llama_new_context_with_model: max tensor size = 102.54 MiB
ggml_metal_add_buffer: allocated 'data ' buffer, size = 4166.08 MiB, ( 4166.70 / 10922.67)
ggml_metal_add_buffer: allocated 'kv ' buffer, size = 256.02 MiB, ( 4422.72 / 10922.67)
ggml_metal_add_buffer: allocated 'alloc ' buffer, size = 156.02 MiB, ( 4578.73 / 10922.67)
AVX = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | SSSE3 = 0 | VSX = 0 |
Llama.generate: prefix-match hit
llama_print_timings: load time = 6058.69 ms
llama_print_timings: sample time = 8.07 ms / 92 runs ( 0.09 ms per token, 11397.42 tokens per second)
llama_print_timings: prompt eval time = 0.00 ms / 1 tokens ( 0.00 ms per token, inf tokens per second)
llama_print_timings: eval time = 5194.35 ms / 92 runs ( 56.46 ms per token, 17.71 tokens per second)
llama_print_timings: total time = 5312.81 ms
Exception ignored in: <function Llava15ChatHandler.__del__ at 0x105891f70>
Traceback (most recent call last):
File "/Users/furm/projects/watcher/venv/lib/python3.9/site-packages/llama_cpp/llama_chat_format.py", line 996, in __del__
File "/Users/furm/projects/watcher/venv/lib/python3.9/site-packages/llama_cpp/_utils.py", line 20, in __enter__
LookupError: unknown encoding: ascii
ggml_metal_free: deallocating
clip_model_load: total allocated memory: 195.95 MB
encode_image_with_clip: image encoded in 1890.27 ms by CLIP ( 3.28 ms per image patch)Apologies if the comment is too long... But it doesn't seem to be an issue with the models or the .png file as it works with just bare llama.cpp and llava-cli
sweetcard
commented
8 months ago verbose = False
remove this line :
verbose = False
It works again. 😄
W1nterFloW
commented
8 months ago I have the similar problem, when I use code below to load llama model:
llm = Llama(model_path="./models/llama-2-7b-chat.Q4_K_M.gguf", verbose=False, n_gpu_layers=30)
Then an exception will be throwed:
Exception ignored in: <function _LlamaContext.del at 0x7f69f2214c10> Traceback (most recent call last): File "/home/lwc/anaconda3/envs/gpu_llama/lib/python3.9/site-packages/llama_cpp/llama.py", line 425, in del File "/home/lwc/anaconda3/envs/gpu_llama/lib/python3.9/site-packages/llama_cpp/_utils.py", line 24, in enter LookupError: unknown encoding: ascii Exception ignored in: <function _LlamaModel.del at 0x7f69f2212ca0> Traceback (most recent call last): File "/home/lwc/anaconda3/envs/gpu_llama/lib/python3.9/site-packages/llama_cpp/llama.py", line 241, in del File "/home/lwc/anaconda3/envs/gpu_llama/lib/python3.9/site-packages/llama_cpp/_utils.py", line 24, in enter LookupError: unknown encoding: ascii Exception ignored in: <function _LlamaBatch.del at 0x7f69f2216f70> Traceback (most recent call last): File "/home/lwc/anaconda3/envs/gpu_llama/lib/python3.9/site-packages/llama_cpp/llama.py", line 675, in del File "/home/lwc/anaconda3/envs/gpu_llama/lib/python3.9/site-packages/llama_cpp/_utils.py", line 24, in enter LookupError: unknown encoding: ascii
problem will not occur when verbose=True
zoldaten
commented
4 months ago i have the same problem on windows 10:
Llama.generate: prefix-match hit
llama_print_timings: load time = 2447.45 ms
llama_print_timings: sample time = 22.66 ms / 104 runs ( 0.22 ms per token, 4589.79 tokens per second)
llama_print_timings: prompt eval time = 0.00 ms / 1 tokens ( 0.00 ms per token, inf tokens per second)
llama_print_timings: eval time = 18997.99 ms / 104 runs ( 182.67 ms per token, 5.47 tokens per second)
llama_print_timings: total time = 19263.97 ms / 105 tokens
Exception ignored in: <function Llava15ChatHandler.__del__ at 0x0000026845EBCF70>
Traceback (most recent call last):
File "C:\Users\{}\.pyenv\pyenv-win\versions\3.10.9\lib\site-packages\llama_cpp\llama_chat_format.py", line 2171, in __del__
File "C:\Users\{}\.pyenv\pyenv-win\versions\3.10.9\lib\site-packages\llama_cpp\_utils.py", line 38, in __enter__
ValueError: I/O operation on closed fileverbose=True or verbose=False doesnt fix it.
Prerequisites
Please answer the following questions for yourself before submitting an issue.
Expected Behavior
Llava should be loading however following the README example I am getting the following error:
clip_model_load: total allocated memory: 195.95 MB Traceback (most recent call last): File "/Users/furm/projects/watcher/main.py", line 26, in <module> llm.create_chat_completion( File "/Users/furm/projects/watcher/venv/lib/python3.9/site-packages/llama_cpp/llama.py", line 2017, in create_chat_completion return handler( File "/Users/furm/projects/watcher/venv/lib/python3.9/site-packages/llama_cpp/llama_chat_format.py", line 1049, in __call__ assert ( AssertionError Exception ignored in: <function Llava15ChatHandler.__del__ at 0x105d1cf70> Traceback (most recent call last): File "/Users/furm/projects/watcher/venv/lib/python3.9/site-packages/llama_cpp/llama_chat_format.py", line 996, in __del__ File "/Users/furm/projects/watcher/venv/lib/python3.9/site-packages/llama_cpp/_utils.py", line 20, in __enter__ LookupError: unknown encoding: ascii ggml_metal_free: deallocatingI'm really not too sure what I'm doing wrong... I'm using the code directly from the README, Both the CLIP model and Llava model are in .gguf format.
Environment and Context
Using a M2 Macbook Air, Python 3.9.6.