Instructions to use KikoCis/FastContext-1.0-4B-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KikoCis/FastContext-1.0-4B-SFT with Transformers:

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

pipe = pipeline("text-generation", model="KikoCis/FastContext-1.0-4B-SFT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KikoCis/FastContext-1.0-4B-SFT")
model = AutoModelForCausalLM.from_pretrained("KikoCis/FastContext-1.0-4B-SFT")
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 KikoCis/FastContext-1.0-4B-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KikoCis/FastContext-1.0-4B-SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KikoCis/FastContext-1.0-4B-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/KikoCis/FastContext-1.0-4B-SFT

SGLang

How to use KikoCis/FastContext-1.0-4B-SFT 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 "KikoCis/FastContext-1.0-4B-SFT" \
    --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": "KikoCis/FastContext-1.0-4B-SFT",
		"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 "KikoCis/FastContext-1.0-4B-SFT" \
        --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": "KikoCis/FastContext-1.0-4B-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use KikoCis/FastContext-1.0-4B-SFT with Docker Model Runner:
```
docker model run hf.co/KikoCis/FastContext-1.0-4B-SFT
```

PRESERVED ORIGINAL // REMOVED BY MICROSOFT FROM HF + GITHUB // MIT
   microsoft/FastContext ──▶ 404
   github.com/microsoft/FastContext ──▶ 404
              │
              ▼
   ┌──────────────────────────┐
   │  weights preserved here  │
   │  bf16 · 8.0 GB · intact  │
   └──────────────────────────┘
     you can't un-open-source
FASTCONTEXT-1.0-4B-SFT
PRESERVED ORIGINAL WEIGHTS · QWEN3 DENSE 4B · 256K CONTEXT · BF16 · 8.0 GB

    
      WEIGHTS
BF16 · UNMODIFIED

      ARCH
QWEN3 DENSE · 36L

      CONTEXT
256K NATIVE

      LICENSE
MIT

    

Microsoft open-sourced FastContext under MIT, then deleted it from both HuggingFace and GitHub about two weeks later (verified: 404 on both, 2026-07-02). MIT means preservation is legal — so here it is, unmodified. Own your AI: a model on your disk can't be sunset by a quarterly review.

🔍 What it is

A repository-exploration subagent for coding agents. Invoked on demand by your main agent, it fires parallel read-only tool calls (READ / GLOB / GREP) across a repo and returns only the file paths + line ranges that matter, as compact context. Your frontier coding agent stops wasting its context window (and your bill) crawling the file tree.

Microsoft's (now-deleted) announcement reported ~60% fewer tokens from the main coding agent and +5.5% on SWE-bench — their figures; the source no longer exists to cite.

Architecture: plain Qwen3ForCausalLM dense 4B — 36 layers, 256K native context. No exotic modules; loads with standard transformers.

🚀 Quick start

from transformers import AutoModelForCausalLM, AutoTokenizer
m = AutoModelForCausalLM.from_pretrained("KikoCis/FastContext-1.0-4B-SFT", torch_dtype="bfloat16", device_map="auto")
tok = AutoTokenizer.from_pretrained("KikoCis/FastContext-1.0-4B-SFT")

Don't want 8 GB? Grab the GGUF quants (1.96–2.5 GB, long-context imatrix, retrieval-validated 30/30 vs this bf16): 👉 KikoCis/FastContext-1.0-4B-longctx-imatrix-GGUF

⚠️ Good to know

It's a scout, not a solver — it finds and returns evidence; pair it with a main coding agent that writes the actual fix.
Upstream docs, harness code and issues were deleted along with the repos; usage conventions here come from the announcement and community mirrors.
Weights are byte-identical to the (re-uploaded) original — no fine-tuning, no edits.

📚 Credit & license

Model, weights, training: © Microsoft (MIT). This is a preservation mirror sourced via the ShaunGves/FastContext-1.0-4B-SFT re-upload after microsoft/FastContext-1.0-4B-SFT was removed. Nothing modified. Quantized companion + validation: KikoCis.

Downloads last month: -

Safetensors

Model size

4B params

Tensor type

BF16