Instructions to use runtime-contracts/mistral-7b-knapsack-lora-persistent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use runtime-contracts/mistral-7b-knapsack-lora-persistent with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.3")
model = PeftModel.from_pretrained(base_model, "runtime-contracts/mistral-7b-knapsack-lora-persistent")

Transformers

How to use runtime-contracts/mistral-7b-knapsack-lora-persistent with Transformers:

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

pipe = pipeline("text-generation", model="runtime-contracts/mistral-7b-knapsack-lora-persistent")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("runtime-contracts/mistral-7b-knapsack-lora-persistent", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use runtime-contracts/mistral-7b-knapsack-lora-persistent with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "runtime-contracts/mistral-7b-knapsack-lora-persistent"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "runtime-contracts/mistral-7b-knapsack-lora-persistent",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/runtime-contracts/mistral-7b-knapsack-lora-persistent

SGLang

How to use runtime-contracts/mistral-7b-knapsack-lora-persistent 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 "runtime-contracts/mistral-7b-knapsack-lora-persistent" \
    --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": "runtime-contracts/mistral-7b-knapsack-lora-persistent",
		"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 "runtime-contracts/mistral-7b-knapsack-lora-persistent" \
        --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": "runtime-contracts/mistral-7b-knapsack-lora-persistent",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use runtime-contracts/mistral-7b-knapsack-lora-persistent with Docker Model Runner:
```
docker model run hf.co/runtime-contracts/mistral-7b-knapsack-lora-persistent
```

mistral-7b-knapsack-lora-persistent

Supplementary release for the paper Evaluating Agents Across Runtime Contracts: When Mismatch Costs Efficiency or Quality (IAEval 2026, the NeurIPS 2026 Workshop on Evaluation of Interactive Agents). 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 paper.

Base model: mistralai/Mistral-7B-v0.3
Training regime: persistent (trained with a persistent Python interpreter runtime (state carries over across agent turns))
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	16384
sample_packing	false
seed	3407
training data	paired traces for the "persistent" regime (see paper Appendix for pairing/filtering procedure)

Mistral-7B-v0.3 ships no chat_template -- trained with Axolotl's built-in mistral_v2v3 template; serving/eval uses a hand-written template extending it with system-role support (folded into the next user turn, since mistral_v2v3 only accepts user/assistant).

Provenance

Released alongside the paper Evaluating Agents Across Runtime Contracts: When Mismatch Costs Efficiency or Quality (IAEval 2026, the NeurIPS 2026 Workshop on Evaluation of Interactive Agents), to reproduce its reported results.

License

Apache License 2.0. The base model (Mistral-7B-v0.3) is also released under Apache 2.0.

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Evaluating Agents Across Runtime Contracts

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Evaluation traces, instance-paired teacher traces, and the 14 LoRA adapters (persistent and stateless per family) behind the paper. • 16 items • Updated about 12 hours ago