How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="haffner/VL-1-Coder-Heretic")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)
# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("haffner/VL-1-Coder-Heretic")
model = AutoModelForMultimodalLM.from_pretrained("haffner/VL-1-Coder-Heretic")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))
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About

Heretic'ed safetensors and GGUF quants of https://huggingface.co/vectionlabs/VL-1-Coder

Refusals: 2/100, KL divergence: 0.0350

  • Parameters:
    • direction_index = 23.30
    • attn.o_proj.max_weight = 1.49
    • attn.o_proj.max_weight_position = 17.90
    • attn.o_proj.min_weight = 1.47
    • attn.o_proj.min_weight_distance = 13.71
    • mlp.down_proj.max_weight = 1.44
    • mlp.down_proj.max_weight_position = 23.74
    • mlp.down_proj.min_weight = 1.09
    • mlp.down_proj.min_weight_distance = 13.05
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