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Model: Phani1479432/rocky-gemma-2b
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2026-07-09 08:57:10 +08:00
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FROM hf.co/Phani1479432/rocky-gemma-2b:rocky-q4_k_m.gguf
# Custom template to match Gemma 2 formatting and Rocky training prefix
TEMPLATE """{{- if .Prompt }}<start_of_turn>user
Speak like Rocky.
{{ .Prompt }}<end_of_turn>
{{- end }}<start_of_turn>model
{{ .Response }}<end_of_turn>
"""
# Set stop tokens
PARAMETER stop "<start_of_turn>"
PARAMETER stop "<end_of_turn>"

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---
license: gemma
library_name: mlx
pipeline_tag: text-generation
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: To access Gemma on Hugging Face, youre required to review and
agree to Googles usage license. To do this, please ensure youre logged in to Hugging
Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
tags:
- conversational
- mlx
- ollama
base_model: google/gemma-2-2b-it
---
# Rocky Gemma 2B (It)
Rocky Gemma 2B is a fine-tuned model based on `google/gemma-2-2b-it`, customized to speak and act like Rocky.
## 🦙 Run with Ollama
You can download and run this model instantly on your local Ollama instance with a single command:
```bash
curl -sSL https://huggingface.co/Phani1479432/rocky-gemma-2b/raw/main/Modelfile | ollama create rocky -f - && ollama run rocky
```
### What this command does:
1. **Downloads the Modelfile**: Fetches the custom configuration specifying stop parameters and formatting templates directly from Hugging Face.
2. **Creates the Model locally**: Registers a new local model named `rocky` in Ollama.
3. **Downloads the GGUF Weights**: Automatically pulls the quantized GGUF weights (`rocky-q4_k_m.gguf`) directly from Hugging Face.
4. **Starts the Chat**: Launches an interactive shell session for you to chat with Rocky.
---
## ⚡ Run with MLX
If you are on Apple Silicon, you can use the MLX library to run the fused model weights:
```bash
pip install mlx-lm
python -m mlx_lm.generate --model Phani1479432/rocky-gemma-2b --prompt "Hey Rocky, what's your training routine like?"
```

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{
"adapter_path": "./adapters",
"batch_size": 2,
"clear_cache_threshold": 0,
"config": null,
"data": "./rocky_data",
"fine_tune_type": "lora",
"grad_accumulation_steps": 1,
"grad_checkpoint": false,
"iters": 600,
"learning_rate": 1e-05,
"lora_parameters": {
"rank": 8,
"dropout": 0.0,
"scale": 20.0
},
"lr_schedule": null,
"mask_prompt": false,
"max_seq_length": 2048,
"model": "google/gemma-2-2b-it",
"num_layers": 16,
"optimizer": "adam",
"optimizer_config": {
"adam": {},
"adamw": {},
"muon": {},
"sgd": {},
"adafactor": {}
},
"project_name": null,
"report_to": null,
"resume_adapter_file": null,
"save_every": 100,
"seed": 0,
"steps_per_eval": 50,
"steps_per_report": 10,
"test": false,
"test_batches": 500,
"train": true,
"val_batches": 25
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{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '
' + message['content'] | trim + '<end_of_turn>
' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model
'}}{% endif %}

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"use_cache": true,
"vocab_size": 256000
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