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Model: intellectlabs/Kepler-8B-Instruct-v2
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+base_model: intellectlabs/Kepler-8B-Instruct-v2
+tags:
+- gguf
+- llama.cpp
+- quantized
+- merge
+- mergekit
+- qwen2
+- text-generation
+- conversational
+language:
+- en
+pipeline_tag: text-generation
+---
+
+<div align="center">
+
+# 🪐 Kepler-8B-Instruct — GGUF
+
+### Quantized GGUF builds of Kepler-8B-Instruct — ready to run anywhere llama.cpp does.
+
+**License: Apache 2.0**  ·  **Format: GGUF**  ·  **Base: [Kepler-8B-Instruct](https://huggingface.co/intellectlabs/Kepler-8B-Instruct)**
+
+**[Original Model](https://huggingface.co/intellectlabs/Kepler-8B-Instruct)** · **[llama.cpp](https://github.com/ggerganov/llama.cpp)** · **[Discussions](../../discussions)**
+
+</div>
+
+---
+
+## ✨ What is this?
+
+**Kepler-8B-Instruct** is an 8-billion-parameter assistant built by merging `Qwen/Qwen2.5-7B-Instruct` (85%) with `deepseek-ai/DeepSeek-R1-Distill-Qwen-7B` (15%) using [mergekit](https://github.com/arcee-ai/mergekit). The blend keeps Qwen2.5-Instruct's clean, reliable instruction-following as the foundation while folding in a touch of DeepSeek's reasoning-distilled behavior.
+
+This repository packages that model as **GGUF** — the universal format for [llama.cpp](https://github.com/ggerganov/llama.cpp) and everything built on it. One file, no Python environment, no CUDA setup. It runs on a gaming laptop, a Raspberry Pi-class board, or a headless server with equal ease.
+
+**Highlights:**
+- 🧩 A reliable instruction-following foundation with a light touch of reasoning-model flavor
+- ⚡ Seven precision levels included, from a ~3 GB ultra-light build to full-precision F16
+- 🔌 Drop-in compatible with llama.cpp, LM Studio, Ollama, koboldcpp, GPT4All, Jan, and text-generation-webui
+- 🗂️ Native ChatML support, with a built-in default identity — ask "who are you" and it answers as Kepler by intellectlabs, in whatever language you ask in
+
+## 🚀 Quick Start
+
+### llama.cpp
+```bash
+./llama-cli -hf intellectlabs/Kepler-8B-Instruct-GGUF:Q4_K_M -p "Explain quantum entanglement simply."
+```
+
+Or download a specific file manually:
+```bash
+huggingface-cli download intellectlabs/Kepler-8B-Instruct-GGUF Kepler-8B-Instruct-Q4_K_M.gguf --local-dir .
+./llama-cli -m Kepler-8B-Instruct-Q4_K_M.gguf -p "Hello, who are you?" -cnv
+```
+
+### LM Studio
+Search **`intellectlabs/Kepler-8B-Instruct-GGUF`** directly in the LM Studio model search bar and download your preferred quant.
+
+### Ollama
+```bash
+ollama run hf.co/intellectlabs/Kepler-8B-Instruct-GGUF:Q4_K_M
+```
+
+## 📦 Available Quantizations
+
+| File | Quant | Size (approx.) | Quality | Recommended For |
+|---|---|---|---|---|
+| `Kepler-8B-Instruct-Q2_K.gguf` | Q2_K | ~3.1 GB | ⭐️ | Extreme low-RAM devices only |
+| `Kepler-8B-Instruct-Q3_K_M.gguf` | Q3_K_M | ~4.0 GB | ⭐️⭐️ | Low-RAM systems, testing |
+| `Kepler-8B-Instruct-Q4_0.gguf` | Q4_0 | ~4.7 GB | ⭐️⭐️⭐️ | Legacy-hardware compatibility |
+| `Kepler-8B-Instruct-Q4_K_M.gguf` | Q4_K_M | ~4.9 GB | ⭐️⭐️⭐️⭐️ | **Best balance — recommended default** |
+| `Kepler-8B-Instruct-Q5_K_M.gguf` | Q5_K_M | ~5.7 GB | ⭐️⭐️⭐️⭐️ | Higher quality, still efficient |
+| `Kepler-8B-Instruct-Q8_0.gguf` | Q8_0 | ~8.5 GB | ⭐️⭐️⭐️⭐️⭐️ | Near-lossless, if you have the RAM |
+| `Kepler-8B-Instruct-F16.gguf` | F16 | ~16 GB | ⭐️⭐️⭐️⭐️⭐️ | Full precision, GPU/server use |
+
+> 💡 **New here?** Start with **Q4_K_M** — it's the sweet spot most people use: small enough to run comfortably, strong enough to feel close to the full model.
+
+## 🧠 Prompt Format
+
+This model uses the **ChatML** template (inherited from its Qwen2 base):
+
+```
+<|im_start|>system
+You are a helpful assistant.<|im_end|>
+<|im_start|>user
+{prompt}<|im_end|>
+<|im_start|>assistant
+```
+
+Most tools (llama.cpp `-cnv`, LM Studio, Ollama) apply this automatically via the embedded chat template — no manual formatting needed.
+
+## 🛠️ How It Was Made
+
+1. **Merge** — `deepseek-ai/DeepSeek-R1-Distill-Qwen-7B` linearly blended into `Qwen/Qwen2.5-7B-Instruct` (15%/85%) using mergekit, with the tokenizer taken from the base model.
+2. **Convert** — merged safetensors converted to GGUF (F16) using `llama.cpp`'s `convert_hf_to_gguf.py`.
+3. **Quantize** — F16 GGUF quantized into multiple formats using `llama-quantize`, covering everything from ultra-compact (Q2_K) to near-lossless (Q8_0).
+
+## ⚠️ Limitations
+
+- This is a **merge**, not a model trained from scratch — behavior is a blend of its parent models and may inherit their quirks or biases.
+- Lower-bit quantizations (Q2_K, Q3_K_M) trade quality for size; expect more inconsistency at extreme compression.
+- Not evaluated on standard benchmarks yet — treat outputs with normal LLM caution, especially for factual or high-stakes use.
+
+## 🙏 Credits
+
+- Base models: [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct), [deepseek-ai/DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B)
+- Merge tooling: [mergekit](https://github.com/arcee-ai/mergekit)
+- Quantization tooling: [llama.cpp](https://github.com/ggerganov/llama.cpp)
+
+---
+
+<div align="center">
+
+**If this was useful, a ⭐️ on the repo helps others find it.**
+
+</div>

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 3584,
+  "initializer_range": 0.02,
+  "intermediate_size": 18944,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen2",
+  "num_attention_heads": 28,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "pad_token_id": null,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "transformers_version": "5.0.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151665
+}

mergekit_config.yml

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+
+slices:
+  - sources:
+      - model: Qwen/Qwen2.5-7B-Instruct
+        layer_range: [0, 28]
+      - model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
+        layer_range: [0, 28]
+merge_method: slerp
+base_model: Qwen/Qwen2.5-7B-Instruct
+parameters:
+  t:
+    - value: 0.5
+dtype: bfloat16
+tokenizer_source: base

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tokenizer.json

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+oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
+size 11421892

tokenizer_config.json

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+}