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USAGE.md

@@ -1,6 +1,6 @@
-# Using marvy-14B
+# Using marvy-1-14B
 
-marvy-14B is a ServiceNow delivery specialist. This guide covers every common
+marvy-1-14B is a ServiceNow delivery specialist. This guide covers every common
 way to run it — cloud or fully local — plus how to wire it into OpenCode.
 
 - [Choosing a format](#choosing-a-format)
@@ -11,7 +11,7 @@ way to run it — cloud or fully local — plus how to wire it into OpenCode.
 - [LM Studio (GUI + local server)](#lm-studio-gui--local-server)
 - [Ollama / llama.cpp (GGUF)](#ollama--llamacpp-gguf)
 - [LoRA adapter (apply on the base)](#lora-adapter-apply-on-the-base)
-- [Use marvy-14B in OpenCode](#use-marvy-14b-in-opencode)
+- [Use marvy-1-14B in OpenCode](#use-marvy-14b-in-opencode)
 - [Prompt recipes per task](#prompt-recipes-per-task)
 
 ---
@@ -20,10 +20,10 @@ way to run it — cloud or fully local — plus how to wire it into OpenCode.
 
 | You want… | Use | Repo |
 |---|---|---|
-| Max quality, GPU/server | Merged FP16 | `MainStack/marvy-14B` |
-| Apple Silicon, native speed | Merged (MLX) | `MainStack/marvy-14B` |
-| Laptop / CPU / Ollama / LM Studio | GGUF (Q4_K_M or Q8_0) | `MainStack/marvy-14B-GGUF` |
-| Smallest download, compose yourself | LoRA adapter (~175 MB) | `MainStack/marvy-14B-lora` |
+| Max quality, GPU/server | Merged FP16 | `MainStack/marvy-1-14B` |
+| Apple Silicon, native speed | Merged (MLX) | `MainStack/marvy-1-14B` |
+| Laptop / CPU / Ollama / LM Studio | GGUF (Q4_K_M or Q8_0) | `MainStack/marvy-1-14B-GGUF` |
+| Smallest download, compose yourself | LoRA adapter (~175 MB) | `MainStack/marvy-1-14B-lora` |
 
 ---
 
@@ -52,7 +52,7 @@ professional English.
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
-model_id = "MainStack/marvy-14B"
+model_id = "MainStack/marvy-1-14B"
 tok = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")
 
@@ -70,12 +70,12 @@ print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))
 
 ```bash
 pip install vllm
-vllm serve MainStack/marvy-14B --served-model-name marvy-14B
+vllm serve MainStack/marvy-1-14B --served-model-name marvy-1-14B
 ```
 
 ```bash
 curl -s http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{
-  "model": "marvy-14B", "temperature": 0.4,
+  "model": "marvy-1-14B", "temperature": 0.4,
   "messages": [
     {"role":"system","content":"You are a senior ServiceNow delivery consultant. ..."},
     {"role":"user","content":"Draft the Incident Management section of an SDD."}
@@ -88,22 +88,22 @@ curl -s http://localhost:8000/v1/chat/completions -H "Content-Type: application/
 pip install mlx-lm
 
 # one-off
-python -m mlx_lm generate --model MainStack/marvy-14B \
+python -m mlx_lm generate --model MainStack/marvy-1-14B \
   --system-prompt "You are a senior ServiceNow delivery consultant. ..." \
   --prompt "Write test cases for a Major Incident workflow." --max-tokens 1024 --temp 0.4
 
 # OpenAI-compatible server
-python -m mlx_lm server --model MainStack/marvy-14B --port 8080
+python -m mlx_lm server --model MainStack/marvy-1-14B --port 8080
 ```
 
 ## LM Studio (GUI + local server)
 
-1. **Install the model** — either search `MainStack/marvy-14B-GGUF` in the
+1. **Install the model** — either search `MainStack/marvy-1-14B-GGUF` in the
    in-app model browser, or place a local copy under
-   `~/.lmstudio/models/MainStack/marvy-14B/` (MLX or GGUF layout).
+   `~/.lmstudio/models/MainStack/marvy-1-14B/` (MLX or GGUF layout).
 2. **Load** it from the GUI, or:
    ```bash
-   lms load MainStack/marvy-14B
+   lms load MainStack/marvy-1-14B
    lms server start          # OpenAI-compatible on http://localhost:1234/v1
    ```
 3. In the Chat tab, set the system prompt (above) and temperature ~0.4.
@@ -112,10 +112,10 @@ python -m mlx_lm server --model MainStack/marvy-14B --port 8080
 
 ```bash
 # Ollama — pull straight from the Hub
-ollama run hf.co/MainStack/marvy-14B-GGUF:Q4_K_M
+ollama run hf.co/MainStack/marvy-1-14B-GGUF:Q4_K_M
 
 # llama.cpp
-llama-cli -hf MainStack/marvy-14B-GGUF:Q4_K_M \
+llama-cli -hf MainStack/marvy-1-14B-GGUF:Q4_K_M \
   -p "Write a user story with acceptance criteria for P1 SLA escalation." --temp 0.4
 ```
 
@@ -139,19 +139,19 @@ from peft import PeftModel
 from transformers import AutoModelForCausalLM, AutoTokenizer
 base = "Qwen/Qwen2.5-14B-Instruct"
 model = AutoModelForCausalLM.from_pretrained(base, torch_dtype="auto", device_map="auto")
-model = PeftModel.from_pretrained(model, "MainStack/marvy-14B-lora")
+model = PeftModel.from_pretrained(model, "MainStack/marvy-1-14B-lora")
 ```
 
 ---
 
-## Use marvy-14B in OpenCode
+## Use marvy-1-14B in OpenCode
 
 marvy runs behind any OpenAI-compatible endpoint (LM Studio, mlx_lm server,
 vLLM). Register it as a custom provider in `opencode.json`.
 
 1. **Start a local server** (LM Studio shown; adjust port for others):
    ```bash
-   lms load MainStack/marvy-14B && lms server start   # http://localhost:1234/v1
+   lms load MainStack/marvy-1-14B && lms server start   # http://localhost:1234/v1
    ```
 2. **Add the provider** to your project `opencode.json` (or global
    `~/.config/opencode/opencode.json`):
@@ -163,15 +163,15 @@ vLLM). Register it as a custom provider in `opencode.json`.
          "name": "LM Studio (local)",
          "options": { "baseURL": "http://localhost:1234/v1" },
          "models": {
-           "marvy-14B": { "name": "marvy-14B (ServiceNow delivery)" }
+           "marvy-1-14B": { "name": "marvy-1-14B (ServiceNow delivery)" }
          }
        }
      }
    }
    ```
-3. **Select** `lmstudio/marvy-14B` in the OpenCode model picker.
+3. **Select** `lmstudio/marvy-1-14B` in the OpenCode model picker.
 
-> marvy-14B is a drafting specialist, not a tool-use/agentic fine-tune. It excels
+> marvy-1-14B is a drafting specialist, not a tool-use/agentic fine-tune. It excels
 > at producing delivery artifacts inside chat; for MCP tool-calling agent loops,
 > keep a frontier model as the orchestrator and switch to marvy for drafting.