recommend

Instructions

Implementation Reference

• mcp_turboquant/server.py:156-180 (handler)

  The MCP tool handler decorated with @mcp.tool(). It accepts a model name, fetches model info, checks dependencies, and delegates to recommend_format().

  @mcp.tool()
  def recommend(model: str) -> dict[str, Any]:
      """Recommend best quantization format and bit width for a model.
  
      Analyzes the model size and your hardware (GPU VRAM, Apple Silicon,
      system RAM) to suggest the optimal format (GGUF/GPTQ/AWQ) and bit
      width (2-8). Ranked recommendations with use-case explanations.
  
      Args:
          model: HuggingFace model ID (e.g. 'meta-llama/Llama-3.1-8B-Instruct')
                 or local path to a model directory.
  
      Returns:
          Ranked recommendations with format, bits, reasoning, and use cases.
      """
      model_info = get_model_info(model)
  
      if not model_info.get("found"):
          return {
              "error": f"Model not found: {model_info.get('error', 'unknown')}",
              "model": model,
          }
  
      deps = check_dependencies()
      return recommend_format(model_info, deps)

• mcp_turboquant/model_info.py:218-328 (helper)

  Core recommendation logic. Takes model_info and dependency data (CUDA, MPS, RAM), evaluates model size against hardware, and builds ranked recommendations (AWQ, GPTQ, GGUF) with bit widths, reasoning, and use cases.

  def recommend_format(
      model_info: dict[str, Any], deps: dict[str, Any]
  ) -> list[dict[str, Any]]:
      """Recommend the best quantization format based on hardware and model."""
      model_size_gb = model_info.get("size_bytes", 0) / 1e9
      params = model_info.get("params_estimate", 0)
      params_b = params / 1e9 if params else 0
  
      has_cuda = deps.get("cuda", False)
      gpu_name = deps.get("gpu_name", "")
      gpu_mem = deps.get("gpu_mem_gb", 0)
      has_mps = deps.get("mps", False)
      system_ram = deps.get("system_ram_gb", 0) or get_system_ram_gb()
  
      hardware = {}
      if has_cuda:
          hardware["accelerator"] = f"CUDA GPU: {gpu_name} ({gpu_mem}GB VRAM)"
      elif has_mps:
          hardware["accelerator"] = f"Apple Silicon (MPS) — {system_ram}GB unified memory"
      else:
          hardware["accelerator"] = "None (CPU only)"
      hardware["ram_gb"] = system_ram
  
      recommendations: list[dict[str, Any]] = []
  
      # Estimate quantized sizes
      size_4bit = model_size_gb / 4 if model_size_gb else params_b * 0.5
      size_8bit = model_size_gb / 2 if model_size_gb else params_b * 1.0
  
      source = model_info.get("source", "MODEL")
  
      def _make_rec(rank, label, fmt, bits, reason, use_case):
          return {
              "rank": rank,
              "label": label,
              "format": fmt,
              "bits": bits,
              "reason": reason,
              "use_case": use_case,
              "command": f'quantize(model="{source}", format="{fmt.lower()}", bits={bits})',
          }
  
      if has_cuda and gpu_mem > 0:
          if size_4bit * 1.2 <= gpu_mem:
              recommendations.append(_make_rec(
                  1, "BEST", "AWQ", 4,
                  f"Best GPU throughput. 4-bit model (~{size_4bit:.1f}GB) fits in {gpu_mem}GB VRAM.",
                  "Production GPU serving with vLLM or TGI",
              ))
              recommendations.append(_make_rec(
                  2, "ALSO GOOD", "GPTQ", 4,
                  "Alternative GPU format. Wider tool support than AWQ.",
                  "GPU serving when AWQ isn't available",
              ))
              recommendations.append(_make_rec(
                  3, "ALTERNATIVE", "GGUF", 4,
                  "Universal format. Works with Ollama, LM Studio, llama.cpp.",
                  "Local use, sharing, or CPU fallback",
              ))
          elif size_4bit * 1.2 > gpu_mem and size_4bit <= system_ram:
              recommendations.append(_make_rec(
                  1, "BEST", "GGUF", 4,
                  f"Model too large for {gpu_mem}GB VRAM. GGUF supports CPU+GPU split.",
                  "CPU+GPU hybrid inference via llama.cpp",
              ))
              if params_b > 13:
                  recommendations.append(_make_rec(
                      2, "ALSO GOOD", "GGUF", 2,
                      f"Aggressive compression to fit in {gpu_mem}GB VRAM. Quality trade-off.",
                      "When VRAM is tight and you need GPU acceleration",
                  ))
          else:
              recommendations.append(_make_rec(
                  1, "BEST", "GGUF", 2,
                  "Model requires aggressive compression for your hardware.",
                  "Maximum compression for large models",
              ))
  
      elif has_mps:
          recommendations.append(_make_rec(
              1, "BEST", "GGUF", 4,
              "Best format for Apple Silicon. llama.cpp has Metal acceleration.",
              "Ollama or LM Studio on Mac",
          ))
          if size_8bit <= system_ram * 0.7:
              recommendations.append(_make_rec(
                  2, "ALSO GOOD", "GGUF", 8,
                  f"Higher quality, still fits in {system_ram}GB unified memory.",
                  "Maximum quality on Mac",
              ))
  
      else:
          recommendations.append(_make_rec(
              1, "BEST", "GGUF", 4,
              "Only format that runs well on CPU. Use with Ollama or llama.cpp.",
              "CPU inference via Ollama or llama.cpp",
          ))
          if params_b <= 3 and size_8bit <= system_ram * 0.5:
              recommendations.append(_make_rec(
                  2, "ALSO GOOD", "GGUF", 8,
                  f"Small model ({model_info.get('params_human', '')}). Higher quality fits in RAM.",
                  "Better quality for small models on CPU",
              ))
  
      return {
          "model": source,
          "model_params": model_info.get("params_human", "unknown"),
          "model_size": model_info.get("size_human", "unknown"),
          "hardware": hardware,
          "recommendations": recommendations,
      }

• mcp_turboquant/server.py:156-157 (registration)

  The tool is registered via the @mcp.tool() decorator on the recommend() function using the FastMCP framework.

  @mcp.tool()
  def recommend(model: str) -> dict[str, Any]: