Instructions to use hfunknown/llama31-8b-knapsack-lora-stateless with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hfunknown/llama31-8b-knapsack-lora-stateless with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B")
model = PeftModel.from_pretrained(base_model, "hfunknown/llama31-8b-knapsack-lora-stateless")

Transformers

How to use hfunknown/llama31-8b-knapsack-lora-stateless with Transformers:

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

pipe = pipeline("text-generation", model="hfunknown/llama31-8b-knapsack-lora-stateless")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("hfunknown/llama31-8b-knapsack-lora-stateless", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use hfunknown/llama31-8b-knapsack-lora-stateless with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hfunknown/llama31-8b-knapsack-lora-stateless"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hfunknown/llama31-8b-knapsack-lora-stateless",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/hfunknown/llama31-8b-knapsack-lora-stateless

SGLang

How to use hfunknown/llama31-8b-knapsack-lora-stateless 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 "hfunknown/llama31-8b-knapsack-lora-stateless" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hfunknown/llama31-8b-knapsack-lora-stateless",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "hfunknown/llama31-8b-knapsack-lora-stateless" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hfunknown/llama31-8b-knapsack-lora-stateless",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use hfunknown/llama31-8b-knapsack-lora-stateless with Docker Model Runner:
```
docker model run hf.co/hfunknown/llama31-8b-knapsack-lora-stateless
```

llama31-8b-knapsack-lora-stateless

Anonymous supplementary release for a double-blind workshop submission. This is one of four LoRA adapters (Mistral-7B-v0.3 / Llama-3.1-8B base model x persistent/stateless training regime) fine-tuned on the Opaque Knapsack agentic task, extending a prior single-base-model result (see the sibling Qwen3-8B release) to a second base model family for the same reproducibility review.

Base model: meta-llama/Llama-3.1-8B
Training regime: stateless (trained with a stateless Python interpreter runtime (interpreter state is reset every agent turn))
Seed: 3407

Training configuration

Fine-tuned with Axolotl, LoRA adapter, 4-bit NF4 quantized base:

Hyperparameter	Value
lora_r	64
lora_alpha	128
lora_dropout	0.05
lora_target_modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
learning_rate	1e-4
lr_scheduler	cosine
optimizer	adamw_torch
epochs	3.0
micro_batch_size	1
gradient_accumulation_steps	16
sequence_len	32768
sample_packing	false
seed	3407
training data	paired traces for the "stateless" regime (see paper Appendix for pairing/filtering procedure)

Base checkpoint's own chat-format tokens (e.g. <|eot_id|>) are untrained on this base (non-instruct) checkpoint -- trained and served with a hand-written minimal template using only real trained tokens (BOS/EOS + plain-text role prefixes), not Llama's native instruct template.

Provenance

Released anonymously alongside a NeurIPS workshop submission for reproducibility review. Non-anonymous release (paper citation, full code, full training traces) will follow after the review process concludes.

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