Instructions to use Harsh/collie-ent-direct-0.6b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Harsh/collie-ent-direct-0.6b with Transformers:

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

pipe = pipeline("text-generation", model="Harsh/collie-ent-direct-0.6b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Harsh/collie-ent-direct-0.6b")
model = AutoModelForCausalLM.from_pretrained("Harsh/collie-ent-direct-0.6b", device_map="auto")
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 Harsh/collie-ent-direct-0.6b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Harsh/collie-ent-direct-0.6b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Harsh/collie-ent-direct-0.6b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Harsh/collie-ent-direct-0.6b

SGLang

How to use Harsh/collie-ent-direct-0.6b 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 "Harsh/collie-ent-direct-0.6b" \
    --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": "Harsh/collie-ent-direct-0.6b",
		"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 "Harsh/collie-ent-direct-0.6b" \
        --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": "Harsh/collie-ent-direct-0.6b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Harsh/collie-ent-direct-0.6b with Docker Model Runner:
```
docker model run hf.co/Harsh/collie-ent-direct-0.6b
```

COLLIE enterprise cataloger — 0.6B

COLLIE converts extracted document text into a six-facet JSON catalog card:

{"subject":["database_performance_tuning"],"type":"issue_report",
 "audience":"internal_team","time":"current","purpose":"request",
 "content_flags":["code_snippets","error_traceback"]}

This is the recommended anchor-free, direct-SFT checkpoint from the COLLIE project. It is a descriptive cataloger, not a sensitivity or importance classifier.

Usage

Use the project CLI for the checkpoint's prompt and decoding behavior:

git clone https://github.com/harshsinghal/COLLIE
cd COLLIE
python3.12 -m venv .venv
.venv/bin/pip install "torch>=2.6,<3" "transformers>=5.14.1,<6"
.venv/bin/python collie.py --file report.txt

For direct Transformers use, copy the CLI's SYSTEM prompt. A language-neutral output contract is published with the checkpoint as collie-card.schema.json.

Compatibility for this checkpoint:

Python 3.10+
Transformers 5.14.1 through the latest compatible 5.x release
PyTorch 2.6+

Training data and procedure

Base: Qwen/Qwen3-0.6B
Direct supervised fine-tuning
5,480 public-corpus teacher inputs across email, extracted PDFs, public chat, public source code, Apache JIRA, and system logs
4,655 final training rows after parsing, schema, register-holdout, and compound-value filters
Apache JIRA and LogHub were held out from final training
Source documents are not redistributed because their upstream terms vary

See the repository for sampling code, prompt versions, experimental artifacts, and the negative-result journal.

Evaluation

On the original 3,705-document reference-free faithfulness evaluation, the published run reported:

79% precise emitted subjects
9% wrong emitted subjects
0.39 missed major subjects per document
92% correct artifact type

These values were produced by an LLM judge and should not be interpreted as human-ground-truth accuracy. The repository's evaluation-v2 tooling adds strict response schemas, per-document audit artifacts, source slices, document-clustered bootstrap intervals, a second-judge path, and a human-audit sample. The original split also contained 18 exact train/evaluation duplicate texts; evaluation v2 removes exact and near duplicates. All historical model outputs parsed as JSON, but 3,630/3,705 (98.0%) satisfy the stricter v1.1 card schema; v2 reports the remainder as model failures rather than dropping them.

Limitations

English-dominant training data.
Inputs are text. PDFs must be text-extracted first; the CLI can do this with its documents extra.
The training excerpts were capped at 4,000 characters. The CLI chunks longer documents, but cross-chunk aggregation has not yet received a human benchmark.
The 0.6B model sometimes fills all three subject slots and may add a merely related third subject. The next data revision explicitly penalizes quota filling and rebalances subject cardinality.
Audience, purpose, and temporal orientation can require organizational context absent from the document.
Document text is untrusted input. The CLI delimits it and instructs the model not to follow embedded directions, but small language models are not a security boundary.

Do not use COLLIE as the sole basis for legal, employment, access-control, or security decisions.

Downloads last month: 79

Safetensors

Model size

0.6B params

Tensor type

BF16

Model tree for Harsh/collie-ent-direct-0.6b

Base model

Qwen/Qwen3-0.6B-Base

Finetuned

Qwen/Qwen3-0.6B

Finetuned

(1141)

this model

Harsh
/

collie-ent-direct-0.6b