Instructions to use ishaanranjan/slm-agent-search-hit-ranker-smollm2-135m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ishaanranjan/slm-agent-search-hit-ranker-smollm2-135m with Transformers:

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

pipe = pipeline("text-generation", model="ishaanranjan/slm-agent-search-hit-ranker-smollm2-135m")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ishaanranjan/slm-agent-search-hit-ranker-smollm2-135m")
model = AutoModelForCausalLM.from_pretrained("ishaanranjan/slm-agent-search-hit-ranker-smollm2-135m", 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 ishaanranjan/slm-agent-search-hit-ranker-smollm2-135m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ishaanranjan/slm-agent-search-hit-ranker-smollm2-135m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ishaanranjan/slm-agent-search-hit-ranker-smollm2-135m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ishaanranjan/slm-agent-search-hit-ranker-smollm2-135m

SGLang

How to use ishaanranjan/slm-agent-search-hit-ranker-smollm2-135m 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 "ishaanranjan/slm-agent-search-hit-ranker-smollm2-135m" \
    --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": "ishaanranjan/slm-agent-search-hit-ranker-smollm2-135m",
		"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 "ishaanranjan/slm-agent-search-hit-ranker-smollm2-135m" \
        --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": "ishaanranjan/slm-agent-search-hit-ranker-smollm2-135m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ishaanranjan/slm-agent-search-hit-ranker-smollm2-135m with Docker Model Runner:
```
docker model run hf.co/ishaanranjan/slm-agent-search-hit-ranker-smollm2-135m
```

slm-agent-search-hit-ranker-smollm2-135m / README.md

ishaanranjan

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	---
	license: apache-2.0
	language:
	- en
	library_name: transformers
	pipeline_tag: text-generation
	base_model: HuggingFaceTB/SmolLM2-135M-Instruct
	base_model_relation: finetune
	tags:
	- code
	- coding-agent
	- tool-use
	- function-calling
	- small-language-model
	- full-parameter-finetuning
	- supervised-fine-tuning
	- deterministic-verification
	- safetensors
	- subroutine:search_hit_ranker
	model-index:
	- name: Code Search-Hit Ranker (SmolLM2 135M)
	results:
	- task:
	type: text-generation
	name: Code Search-Hit Ranker
	dataset:
	name: Held-out HTTPX and Jinja2 oracle benchmark
	type: custom
	metrics:
	- type: accuracy
	value: 0.16
	name: Success after one schema-feedback retry
	- type: accuracy
	value: 1.0
	name: First-pass schema validity
	---

	# Code Search-Hit Ranker (SmolLM2 135M)

	This is a full-parameter supervised fine-tune of
	[`HuggingFaceTB/SmolLM2-135M-Instruct`](https://huggingface.co/HuggingFaceTB/SmolLM2-135M-Instruct) for one narrow,
	schema-bound developer-agent subroutine:

	> Pick the definition site among real grep hits for a symbol.

	The model is one cell from the
	[Parameter Floors for Developer-Agent Subroutines](https://github.com/IshaanAyaan/slm-agents)
	experiment. Labels are generated by deterministic oracles over real Python
	repositories; no teacher model or human judge labels the data.

	## Intended Use

	Use this checkpoint inside the repository's verified subroutine harness, which
	renders the task-specific prompt, parses strict JSON, permits one localized
	schema-feedback retry, applies deterministic guards, and falls back to rules
	where appropriate. This is not a general coding assistant or chat model.

	## Evaluation

	Evaluation uses up to 250 examples from HTTPX and Jinja2, both held out
	entirely from training. Decoding is greedy.

	\| Metric \| Result \|
	\|---\|---:\|
	\| Success after one schema retry \| 16.0% \|
	\| First-pass success \| 16.0% \|
	\| First-pass schema validity \| 100.0% \|
	\| Base instruct success after retry \| 0.0% for the base instruct model \|
	\| Rules-only success \| 99.3% \|

	Experiment verdict for this subroutine: rules suffice.

	## Training

	- Training examples: 1576
	- Epochs: 3.0
	- Learning rate: 2e-05
	- Effective batch configuration: 32 per device x
	1 gradient accumulation
	- Maximum sequence length: 2048
	- Seed: 0
	- Final training loss: 1.027056
	- Reproduction hardware: one NVIDIA A100 80GB PCIe
	- Source revision:
	[`d0fd7bf`](https://github.com/IshaanAyaan/slm-agents/commit/d0fd7bff420c2f2f0446599ca2b169cc4f03b06a)

	The dataset was generated from pinned Flask, Click, and Rich repositories for
	training/validation. HTTPX and Jinja2 were reserved for testing.

	## Limitations

	The checkpoint is specialized to one closed JSON schema and should not be
	expected to retain broad instruction-following ability. The experiment mixes
	two base-model families across its size sweep. Some subroutines are better
	served by deterministic rules; consult the verdict above before deployment.

	## License

	Apache-2.0, following the base model. Experiment code is MIT licensed.