Instructions to use badtheorylabs/Tinfield-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use badtheorylabs/Tinfield-1 with Transformers:

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

pipe = pipeline("image-text-to-text", model="badtheorylabs/Tinfield-1")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("badtheorylabs/Tinfield-1")
model = AutoModelForMultimodalLM.from_pretrained("badtheorylabs/Tinfield-1", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use badtheorylabs/Tinfield-1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "badtheorylabs/Tinfield-1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "badtheorylabs/Tinfield-1",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/badtheorylabs/Tinfield-1

SGLang

How to use badtheorylabs/Tinfield-1 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 "badtheorylabs/Tinfield-1" \
    --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": "badtheorylabs/Tinfield-1",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "badtheorylabs/Tinfield-1" \
        --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": "badtheorylabs/Tinfield-1",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use badtheorylabs/Tinfield-1 with Docker Model Runner:
```
docker model run hf.co/badtheorylabs/Tinfield-1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Tinfield 1

Tinfield 1 is an agentic model for terminal work and long-horizon software engineering.

It scores 33.0 on Terminal-Bench 4.0, ahead of Claude Opus 4.8 at 23.6 and Claude Sonnet 5 at 12.4, and 62 on DeepSWE v1.1, ahead of Qwen3.8 Max, DeepSeek V4 Flash and Opus 4.8 again. On the Terminal-Bench board it is the second open-weight model, on a leaderboard otherwise made up entirely of frontier closed models.

It does that on 6.6B active parameters per token out of 177B total, and the quantized builds run on a single 64 GB machine.

Built on Qwen3.8-Flash-Next. 256k context, BF16 weights.

Evaluation

Benchmark	Tinfield 1	Qwen3.8-Flash-Next
Terminal-Bench 4.0	33.0	29.0
DeepSWE v1.1	62.0	58.7

Evaluated with mini-swe-agent at k=5 on the full task sets: 66 tasks for Terminal-Bench 4.0, 113 for DeepSWE v1.1.

Terminal-Bench 4.0

Model	Agent	Score
GLM-5.3 (max)	Claude Code	41.8
GPT-5.6 Sol (max)	Codex	37.3
Tinfield 1	mini-swe-agent	33.0
Qwen3.8-Flash-Next		29.0
Claude Opus 4.8 (max)	Claude Code	23.6
GPT-5.6 Terra (max)	Codex	21.5
Grok 4.6 (high)	Grok Build	20.3
Gemini 3.8 Flash (high)	mini-swe-agent	19.1
Claude Sonnet 5 (max)	Claude Code	12.4

DeepSWE v1.1

Model	Score
GLM-5.3 (max)	69
GLM-5.3 Flash (max)	63
DeepSeek V4 Pro (max)	63
Tinfield 1	62
Claude Opus 4.8 (max)	59
Qwen3.8 Max (xhigh)	57
Muse Spark 1.2 (xhigh)	55
Claude Sonnet 5 (max)	54
DeepSeek V4 Flash (max)	53

Comparison figures from the Terminal-Bench and DeepSWE leaderboards. Base model Terminal-Bench figure from Artificial Analysis; base model DeepSWE figure from the Qwen3.8-Flash-Next model card.

Quantized builds

Build	Size
Tinfield 1 Compact	72 GB	IQ2_XXS gate/up, IQ4_NL down
Tinfield 1 Mini	61 GB	IQ2_XXS gate/up, range-searched Q2_0 down

Both run on a 64 GB machine. Benchmark scores above are for the BF16 weights; the quantized builds have not been evaluated on these benchmarks.

Community builds

Build	Size	By
Tinfield 1 Q4_K_XL	111 GB, 5.03 bpw	needmorevramog

Not built or measured by us. Quantizing your own is covered in QUANTIZING.md.

License

Qwen Community License 1.0, following the base model.

Downloads last month: 504

Safetensors

Model size

180B params

Tensor type

BF16

I64

Model tree for badtheorylabs/Tinfield-1

Base model

Qwen/Qwen3.8-Flash-Next

Finetuned

(56)

this model

Quantizations

5 models