Add files using upload-large-folder tool

README.md

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+---
+base_model: ibm-granite/granite-4.1-3b
+base_model_relation: finetune
+datasets:
+- Glint-Research/Fable-5-traces
+- Roman1111111/gpt5.5-terminal
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- safetensors
+- qlora
+- agentic
+- coding
+- reasoning
+- thinking
+- claude
+- transformers
+- granite
+---
+
+# Parable-Granite-4.1-3B-Claude-Fable-5
+
+<picture>
+  <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/ankit-aglawe/parable-assets/main/parable_header_dark.png">
+  <img alt="Parable" src="https://raw.githubusercontent.com/ankit-aglawe/parable-assets/main/parable_header.png">
+</picture>
+
+**Granite 4.1 3B trained on real Claude Fable 5 and GPT-5.5 agent traces: 87% lower held-out test loss than its base. A compact chat model trained on the prose side of real agent sessions: strongest at explanations, idioms, and one-liners.**
+
+Parable-Granite-4.1-3B is an [ibm-granite/granite-4.1-3b](https://huggingface.co/ibm-granite/granite-4.1-3b) fine-tune trained on real multi-step agent sessions: planning, tool use, and `<think>` reasoning captured from actual Claude Fable 5 and GPT-5.5 agent work, not synthetic Q&A. Smallest and newest release in the Parable series, alongside [Parable-Qwen3-4B](https://huggingface.co/AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF).
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "AnkitAI/Parable-Granite-4.1-3B-Claude-Fable-5"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")
+
+messages = [{"role": "user", "content": "Write a bash one-liner to find the 10 largest files in a directory tree."}]
+inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
+output = model.generate(inputs, max_new_tokens=3000, temperature=0.7, top_p=0.95, do_sample=True)
+print(tokenizer.decode(output[0][inputs.shape[1]:], skip_special_tokens=True))
+```
+
+Output opens with a `<think>...</think>` reasoning block before the final answer. Strip it before showing responses to end users.
+
+**Sampling:** temperature 0.7, top_p 0.95. Budget `max_new_tokens` generously (**at least 2500**): trace-trained reasoning models think at length before answering.
+
+GGUF quants for llama.cpp, Ollama, and LM Studio: [Parable-Granite-4.1-3B-Claude-Fable-5-GGUF](https://huggingface.co/AnkitAI/Parable-Granite-4.1-3B-Claude-Fable-5-GGUF).
+
+## Training data
+
+- [Glint-Research/Fable-5-traces](https://huggingface.co/datasets/Glint-Research/Fable-5-traces): 4.4k real Claude Fable 5 coding-agent session traces with `<think>` reasoning and tool calls (AGPL-3.0)
+- [Roman1111111/gpt5.5-terminal](https://huggingface.co/datasets/Roman1111111/gpt5.5-terminal): terminal-agent task solutions (MIT)
+
+Every example passed a quality gate (schema validation, secrets scrub, length filtering) before training. QLoRA fine-tune (NF4, sequence length 2048) trained on a single 16 GB GPU, quantized with [llama.cpp](https://github.com/ggml-org/llama.cpp).
+
+## Evaluation
+
+![Held-out evals across the Parable family](https://raw.githubusercontent.com/ankit-aglawe/parable-assets/main/parable_evals.png)
+
+Held-out test split, identical evaluation code and context length for base and fine-tune:
+
+| Metric | Base Granite-4.1-3B | Parable | Δ |
+|---|---|---|---|
+| Test loss | 2.824 | **0.376** | **−87%** |
+
+**Qualitative review** (34 coding/terminal/debugging prompts, strictly graded by mentally executing every answer): **14 of 34 fully correct, 26 of 34 correct or partially correct.** The pattern is consistent: reliable on explanations, one-liners, idiomatic refactors, and debugging advice; unreliable on multi-part script and config generation, where we recommend the [8B](https://huggingface.co/AnkitAI/Parable-Granite-4.1-8B-Claude-Fable-5-GGUF) instead. We publish these numbers because strict qualitative grading is rare in this niche; judge accordingly.
+
+For reference, the strongest published fine-tune on this data family (a 9B) reports 0.71 validation loss. Cross-repo numbers are indicative only: splits, tokenizers, and context lengths differ (ours is measured at 1,024 tokens).
+
+## Limitations
+
+- Best in its lane: explanations, one-liners, and idiomatic fixes. For multi-step script or config generation, use the 8B; this model can hallucinate agent-transcript formatting on those prompts (3 of 34 in our eval).
+- Fine-tuned at 2,048-token sequences; the base model's native 128K-token context remains fully available, so long sessions work, with the fine-tuned behavior strongest in the opening turns.
+
+As a fine-tune it inherits Granite-4.1-3B's base behaviors and knowledge cutoff. As with any local model, treat generated commands and code as drafts to review.
+
+## Provenance & licensing
+
+Model weights: **Apache-2.0** (inherited from Granite-4.1-3B). Training data licenses: Fable-5-traces **AGPL-3.0**, gpt5.5-terminal **MIT**. Because those traces originate from third-party assistants, the providers' terms may apply to downstream training and distillation. If you plan to build on this model commercially, confirm your use aligns with those terms.
+
+
+## Get Parable
+
+| Platform | Command / Link |
+|---|---|
+| Ollama | `ollama run parable/fable` ([parable namespace](https://ollama.com/parable)) |
+| Hugging Face | [GGUF quants, full weights, eval reports](https://huggingface.co/collections/AnkitAI/parable-6a4fac60f4b35afca3019621) |
+| LM Studio | search "parable" in-app, or any HF GGUF repo URL |
+
+## Acknowledgements
+
+- [Glint-Research](https://huggingface.co/Glint-Research) and [Roman1111111](https://huggingface.co/Roman1111111) for the open trace datasets
+- [IBM Granite](https://huggingface.co/ibm-granite) for the base model
+- [empero-ai](https://huggingface.co/empero-ai), whose Qwable recipe the Parable series follows
+- [llama.cpp](https://github.com/ggml-org/llama.cpp)

chat_template.jinja

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+{%- set tools_system_message_prefix = 'You are a helpful assistant with access to the following tools. You may call one or more tools to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>'  %}
+{%- set tools_system_message_suffix = '\n</tools>\n\nFor each tool 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>. If a tool does not exist in the provided list of tools, notify the user that you do not have the ability to fulfill the request.' %}
+{%- set documents_system_message_prefix = 'You are a helpful assistant with access to the following documents. You may use one or more documents to assist with the user query.\n\nYou are given a list of documents within <documents></documents> XML tags:\n<documents>' %}
+{%- set documents_system_message_suffix = '\n</documents>\n\nWrite the response to the user\'s input by strictly aligning with the facts in the provided documents. If the information needed to answer the question is not available in the documents, inform the user that the question cannot be answered based on the available data.' %}
+{%- if available_tools is defined and available_tools %}
+    {%- set tools = available_tools %}
+{%- endif %}
+{%- set ns = namespace(tools_system_message=tools_system_message_prefix,
+                       documents_system_message=documents_system_message_prefix,
+                       system_message=''
+                       ) %}
+{%- if tools %}
+    {%- for tool in tools %}
+        {%- set ns.tools_system_message = ns.tools_system_message + '\n' + (tool | tojson) %}
+    {%- endfor %}
+    {%- set ns.tools_system_message = ns.tools_system_message + tools_system_message_suffix %}
+{%- else %}
+    {%- set ns.tools_system_message = '' %}
+{%- endif %}
+{%- if documents %}
+    {%- for document in documents %}
+        {%- set ns.documents_system_message = ns.documents_system_message + '\n' + (document | tojson) %}
+    {%- endfor %}
+    {%- set ns.documents_system_message = ns.documents_system_message + documents_system_message_suffix %}
+{%- else %}
+    {%- set ns.documents_system_message = '' %}
+{%- endif %}
+{%- if messages[0].role == 'system' %}
+    {%- if messages[0].content is string %}
+        {%- set ns.system_message = messages[0].content %}
+    {%- elif messages[0].content is iterable %}
+        {%- for entry in messages[0].content %}
+            {%- if entry.type== 'text' %}
+                {%- if ns.system_message != '' %}
+                    {%- set ns.system_message = ns.system_message + '\n' %}
+                {%- endif %}
+                {%- set ns.system_message = ns.system_message + entry.text %}
+            {%- endif %}
+        {%- endfor %}
+    {%- endif %}
+    {%- if tools and documents %}
+        {%- set ns.system_message = ns.system_message + '\n\n' +  ns.tools_system_message + '\n\n' + ns.documents_system_message %}
+    {%- elif tools %}
+        {%- set ns.system_message = ns.system_message + '\n\n' + ns.tools_system_message %}
+    {%- elif documents %}
+        {%- set ns.system_message = ns.system_message + '\n\n' + ns.documents_system_message %}
+    {%- endif %}
+{%- else %}
+    {%- if tools and documents %}
+        {%- set ns.system_message = ns.tools_system_message + '\n\n' + ns.documents_system_message  %}
+    {%- elif tools %}
+        {%- set ns.system_message = ns.tools_system_message %}
+    {%- elif documents %}
+        {%- set ns.system_message = ns.documents_system_message %}
+    {%- endif %}
+{%- endif %}
+{%- if ns.system_message %}
+    {{- '<|start_of_role|>system<|end_of_role|>' + ns.system_message + '<|end_of_text|>\n' }}
+{%- endif %}
+{%- for message in messages %}
+    {%- set content = namespace(val='') %}
+    {%- if message.content is string %}
+        {%- set content.val = message.content %}
+    {%- else %}
+        {%- if message.content is iterable %}
+            {%- for entry in message.content %}
+                {%- if entry.type== 'text' %}
+                    {%- if content.val != '' %}
+                        {%- set content.val = content.val + '\n' %}
+                    {%- endif %}
+                    {%- set content.val = content.val + entry.text %}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+    {%- endif %}
+    {%- if (message.role == 'user') or (message.role == 'system' and not loop.first) %}
+        {{- '<|start_of_role|>' + message.role + '<|end_of_role|>' + content.val + '<|end_of_text|>\n' }}
+    {%- elif message.role == 'assistant' %}
+        {{- '<|start_of_role|>' + message.role + '<|end_of_role|>' + content.val }}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content.val) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|end_of_text|>\n' }}
+    {%- elif message.role == 'tool' %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != 'tool') %}
+            {{- '<|start_of_role|>user<|end_of_role|>' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content.val }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != 'tool') %}
+            {{- '<|end_of_text|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|start_of_role|>assistant<|end_of_role|>' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "GraniteForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attention_multiplier": 0.015625,
+  "bos_token_id": 100257,
+  "dtype": "float16",
+  "embedding_multiplier": 12.0,
+  "eos_token_id": 100257,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.1,
+  "intermediate_size": 8192,
+  "logits_scaling": 10.0,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "granite",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 40,
+  "num_key_value_heads": 8,
+  "pad_token_id": 100256,
+  "residual_multiplier": 0.22,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000000,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.57.6",
+  "use_cache": true,
+  "vocab_size": 100352
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 100257,
+  "eos_token_id": 100257,
+  "pad_token_id": 100256,
+  "transformers_version": "4.57.6"
+}

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special_tokens_map.json

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tokenizer_config.json

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+}