Instructions to use XavierThibaudon/anomaly-analyzer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use XavierThibaudon/anomaly-analyzer with Transformers:

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

pipe = pipeline("text-generation", model="XavierThibaudon/anomaly-analyzer")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XavierThibaudon/anomaly-analyzer")
model = AutoModelForCausalLM.from_pretrained("XavierThibaudon/anomaly-analyzer")
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 XavierThibaudon/anomaly-analyzer with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/XavierThibaudon/anomaly-analyzer

SGLang

How to use XavierThibaudon/anomaly-analyzer 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 "XavierThibaudon/anomaly-analyzer" \
    --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": "XavierThibaudon/anomaly-analyzer",
		"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 "XavierThibaudon/anomaly-analyzer" \
        --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": "XavierThibaudon/anomaly-analyzer",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use XavierThibaudon/anomaly-analyzer with Docker Model Runner:
```
docker model run hf.co/XavierThibaudon/anomaly-analyzer
```

XavierThibaudon commited on Feb 16

Commit

ce964a6

verified ·

1 Parent(s): c544885

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README.md +4 -4

README.md CHANGED Viewed

@@ -10,7 +10,7 @@ tags:
   - crypto-exchange
   - lora
   - fine-tuned
-base_model: Qwen/Qwen2.5-1.5B-Instruct
 pipeline_tag: text-generation
 ---
@@ -22,9 +22,9 @@ A fine-tuned language model for analyzing performance anomalies in distributed c
 | Property | Value |
 |---|---|
-| **Base Model** | [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) |
 | **Method** | LoRA (Low-Rank Adaptation) |
-| **Trainable Parameters** | 1.56M / 1.56B (1.18%) |
 | **Training Framework** | [Axolotl](https://github.com/axolotl-ai-cloud/axolotl) |
 | **Precision** | BF16 with 8-bit quantized base |
 | **License** | Apache 2.0 |
@@ -142,7 +142,7 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 - Trained on 222 examples (22 real + 200 synthetic) — results continue to improve with more real-world data
 - Optimized for the KrystalineX platform's specific service topology (kx-exchange, kx-wallet, api-gateway, order-matcher)
 - Best results when prompts include correlated system metrics alongside trace data
-- Small 1.5B model may not always follow strict output formatting — the parser handles free-form responses gracefully
 - May hallucinate metric interpretations for scenarios not represented in training data
 ## Citation

   - crypto-exchange
   - lora
   - fine-tuned
+base_model: meta-llama/Llama-3.2-1B-Instruct
 pipeline_tag: text-generation
 ---
 | Property | Value |
 |---|---|
+| **Base Model** | [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) |
 | **Method** | LoRA (Low-Rank Adaptation) |
+| **Trainable Parameters** | 1.56M / 1.24B (0.13%) |
 | **Training Framework** | [Axolotl](https://github.com/axolotl-ai-cloud/axolotl) |
 | **Precision** | BF16 with 8-bit quantized base |
 | **License** | Apache 2.0 |
 - Trained on 222 examples (22 real + 200 synthetic) — results continue to improve with more real-world data
 - Optimized for the KrystalineX platform's specific service topology (kx-exchange, kx-wallet, api-gateway, order-matcher)
 - Best results when prompts include correlated system metrics alongside trace data
+- Small 1B model may not always follow strict output formatting — the parser handles free-form responses gracefully
 - May hallucinate metric interpretations for scenarios not represented in training data
 ## Citation