transformerjs

.gitattributes

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.gitignore

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+node_modules
+cache

Dockerfile

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-# Use the official Python slim image
-FROM python:3.8-slim
-
-# Set environment variable for Hugging Face cache
-ENV HF_HOME=/tmp/.huggingface
-
-# Working directory
-WORKDIR /app
-
-# Copy dependencies
-COPY requirements.txt .
-RUN pip install --no-cache-dir -r requirements.txt
-
-# Copy application code
-COPY . .
-
-# Expose the port FastAPI will run on
-EXPOSE 7860
-
-# Run the API
-CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

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----
-title: SmolLM2 135M
-emoji: 🌖
-colorFrom: blue
-colorTo: purple
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from typing import List
-import torch
-
-app = FastAPI(title="Language Model API")
-
-# Model configuration
-CHECKPOINT = "HuggingFaceTB/SmolLM2-135M-Instruct"
-DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
-
-# Initialize model and tokenizer
-try:
-    tokenizer = AutoTokenizer.from_pretrained(CHECKPOINT)
-    model = AutoModelForCausalLM.from_pretrained(CHECKPOINT).to(DEVICE)
-except Exception as e:
-    raise RuntimeError(f"Failed to load model: {str(e)}")
-
-class ChatMessage(BaseModel):
-    role: str
-    content: str
-
-class ChatRequest(BaseModel):
-    messages: List[ChatMessage]
-    max_new_tokens: int = 50
-    temperature: float = 0.2
-    top_p: float = 0.9
-
-@app.post("/generate")
-async def generate_response(request: ChatRequest):
-    try:
-        # Convert messages to the format expected by the model
-        messages = [{"role": msg.role, "content": msg.content} for msg in request.messages]
-        
-        # Prepare input
-        input_text = tokenizer.apply_chat_template(messages, tokenize=False)
-        inputs = tokenizer.encode(input_text, return_tensors="pt").to(DEVICE)
-        
-        # Generate response
-        outputs = model.generate(
-            inputs,
-            max_new_tokens=request.max_new_tokens,
-            temperature=request.temperature,
-            top_p=request.top_p,
-            do_sample=True
-        )
-        
-        # Decode and return response
-        response_text = tokenizer.decode(outputs[0])
-        
-        return {
-            "generated_text": response_text
-        }
-    
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=7860)

index.ts

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+import { pipeline } from "@huggingface/transformers";
+
+function loadingBar(total: number, current: number, barLength = 20) {
+  const filled = Math.round((current / total) * barLength);
+  const empty = barLength - filled;
+
+  const bar = `[${"#".repeat(filled)}${" ".repeat(empty)}] ${Math.round(
+    (current / total) * 100
+  )}%`;
+  process.stdout.write("\r" + bar);
+}
+
+process.stdout.write("\n"); // Move
+
+async function run() {
+  // Load a pre-trained model and tokenizer
+  const pipe = await pipeline(
+    "text-generation",
+    "HuggingFaceTB/SmolLM2-135M-Instruct",
+    // "HuggingFaceTB/SmolLM2-360M-Instruct",
+    // "HuggingFaceTB/SmolLM2-1.7B-Instruct",
+    {
+      cache_dir: "./cache",
+      progress_callback: (progress: any) => {
+        loadingBar(progress.total, progress.loaded);
+      },
+    }
+  );
+
+  // Perform sentiment analysis
+  const result = await pipe(
+    [
+      {
+        role: "user",
+        content: `Recomend a song for me based on these songs: 'Dheere Dheere Aap Mere', 'Zara Zara - Deep House Mix', 'Hey Minnale (From "Amaran") (Tamil)', 'Apna Bana Le (From "Bhediya")`,
+      },
+    ],
+    {
+      max_new_tokens: 100,
+    }
+  );
+
+  console.log(result);
+}
+
+run().catch(console.error);

package.json

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+{
+  "name": "test",
+  "version": "1.0.0",
+  "main": "index.js",
+  "scripts": {
+    "test": "echo \"Error: no test specified\" && exit 1"
+  },
+  "keywords": [],
+  "author": "",
+  "license": "ISC",
+  "type": "commonjs",
+  "description": "",
+  "dependencies": {
+    "@huggingface/transformers": "^3.3.1",
+    "@xenova/transformers": "^2.17.2"
+  }
+}

requirements.txt

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-fastapi
-uvicorn
-transformers
-torch
-peft
-huggingface_hub
-safetensors