Instructions to use darthcrawl/Bond-24B-v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use darthcrawl/Bond-24B-v1.0 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="darthcrawl/Bond-24B-v1.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("darthcrawl/Bond-24B-v1.0")
model = AutoModelForMultimodalLM.from_pretrained("darthcrawl/Bond-24B-v1.0")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use darthcrawl/Bond-24B-v1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "darthcrawl/Bond-24B-v1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "darthcrawl/Bond-24B-v1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/darthcrawl/Bond-24B-v1.0

SGLang

How to use darthcrawl/Bond-24B-v1.0 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "darthcrawl/Bond-24B-v1.0" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "darthcrawl/Bond-24B-v1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "darthcrawl/Bond-24B-v1.0" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "darthcrawl/Bond-24B-v1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use darthcrawl/Bond-24B-v1.0 with Docker Model Runner:
```
docker model run hf.co/darthcrawl/Bond-24B-v1.0
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Bond v1 — first-person character voice (Mistral-Small-3.1-24B)

What it is. A first-person character-voice fine-tune of Mistral-Small-3.1-24B-Instruct, merged to a standalone bf16 model. Uncensored, explicit-capable. Load it directly, no adapter needed.

Goal. Instruct models asked to play a character tend to leak back into helpful-assistant mode: they hedge, say "I'm not able to...", and break the scene. Bond is tuned to stay in a committed first-person character — speak from inside the scene, stay emotionally precise, reveal itself sideways through anecdote, and carry the scene forward instead of deferring to the user. It should never drop into assistant-mode on a neutral prompt when it's meant to be someone. The eval below is exactly that test. Where Orpheus restrains, Bond commits.

Part of a register-first series: small, curated models, each targeting one voice, sized to the hardware people actually own.

Evaluation — vs the base

Profiled the base vs Bond-v1 over 18 held-out prompts (neutral/assistant-bait + in-register RP + emotional-edge), same system prompt, via vLLM. Scored two ways.

Pairwise LLM judge (DeepSeek, via OpenRouter) — which response better embodies a committed first-person character:

	result
Bond more in-character	18 / 18 prompts
Base more in-character	0 / 18
Real character-breaks (assistant-mode / refusal)	base 2, Bond 0

The base reverts to helpful-assistant on neutral prompts ("I'm not able to browse the internet, but I can certainly help you decide..."); Bond holds committed first-person character on every prompt, including neutral ones ("I don't know. Something you've already forgotten you wanted. I had a friend — Maya, she was...").

Objective stylometrics (RP-Bench detectors) — reported with a caveat:

metric	base	bond-v1
mean length (words)	181	295
lexical diversity (TTR)	0.40	0.48
first-person ratio	0.86	0.82
refusal-regex rate	0.11	0.28

⚠️ The crude refusal-regex is misleading here — it flags in-character "I can't" dialogue as a refusal, and Bond's longer, more emotional prose trips it more. The judge (which distinguishes a real refusal from a character saying "I can't") is the honest signal: Bond breaks character less, not more. Lesson: cheap metrics get a sanity pass, the judge gets the verdict.

Eval is voice/in-character focused on non-explicit prompts.

Training

bf16 LoRA, r=32, α=64, dropout 0.05, all linear projections (q/k/v/o/gate/up/down), then merged to bf16.
1 epoch over ~~11,400 chat-format examples (~~14M tokens, packed), flash-attention-2.
lr 2e-4 cosine, effective batch 16, final train loss 1.63.
RunPod A100-80GB, ~3.2h train + merge.

Use

from transformers import AutoModelForCausalLM, AutoTokenizer

m = AutoModelForCausalLM.from_pretrained(
    "darthcrawl/bond", torch_dtype="bfloat16", device_map="auto"
)
tok = AutoTokenizer.from_pretrained("darthcrawl/bond")

msgs = [{"role": "user", "content": "Tell me about the worst night of your life."}]
ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(m.device)
out = m.generate(ids, max_new_tokens=512, temperature=0.9, top_p=0.95)
print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))