darthcrawl/mistral-7b-instruct-v0.3-artisan

# Mistral-7B-Instruct-v0.3 · Artisan

A LoRA fine-tune of `mistralai/Mistral-7B-Instruct-v0.3` tuned for direct, practical assistant responses with a side of grounded character roleplay. Built on a curated seed of hand-written technical Q&A and dialogue, then balanced against general-purpose and conversational sets so the model keeps its broad capabilities.

## What it's good at

• —Technical answers without the boilerplate. Backend / systems / Go / databases / networking — concise, no "as a language model…" preamble.
• —In-character dialogue. Roleplay scenes with continuity, restraint, and tone awareness. Won't break frame.
• —Adult-adjacent content. Open by default; trained with a curated explicit-RP slice. Not for all audiences.
• — ## MLX variants

## Quickstart

  from transformers import AutoModelForCausalLM, AutoTokenizer

  model_id = "darthcrawl/mistral-7b-instruct-v0.3-artisan"
  tokenizer = AutoTokenizer.from_pretrained(model_id)
  model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")

  messages = [
      {"role": "user", "content": "Explain consistent hashing in two paragraphs."},
  ]
  inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(model.device)
  out = model.generate(inputs, max_new_tokens=512, temperature=0.7)
  print(tokenizer.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))

MLX quants (Apple Silicon)

Quantized variants for mlx-lm:

• —4-bit, 4-bit DWQ, 6-bit, 8-bit

Training

Limitations

• —English only.
• —Inherits the base model's biases and knowledge cutoff.
• —Adult RP slice means the model is more permissive than the stock instruct. Use accordingly.

License

Apache 2.0, inherited from the base model.