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Model: abhinav0231/Lily-1.5b-v0.3
Source: Original Platform

.gitattributes

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+*.bz2 filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+license: apache-2.0
+language:
+  - en
+pipeline_tag: text-generation
+tags:
+  - qwen2
+  - causal-lm
+  - instruction-tuned
+  - distillation
+  - sft
+  - lora
+  - qlora
+  - unsloth
+  - chatml
+  - reasoning
+
+base_model: abhinav0231/Lily-1.5b-v0.1
+
+datasets:
+  - abhinav0231/Sarvam-105b-Distill-100k
+---
+
+# Lily-1.5b-v0.3
+
+Lily-1.5b-v0.3 is a distilled instruction-tuned language model built by continuing training from `abhinav0231/Lily-1.5b-v0.1` on the `abhinav0231/Sarvam-105b-Distill-100k` dataset using the `chatml` split/configuration.
+
+This version was trained as an offline supervised fine-tuning run focused on high-quality long-form assistant responses in ChatML format, with many examples following an explicit `<think>` and `<answer>` structure.
+
+The model was trained and merged in a single-GPU Modal workflow on an NVIDIA A100-SXM4-40GB system using BF16, QLoRA, and Unsloth.
+
+---
+
+# Model summary
+
+This checkpoint starts from `abhinav0231/Lily-1.5b-v0.1` and applies a distillation-style supervised fine-tuning stage rather than training from scratch.
+
+The base architecture loaded during training is a Qwen2-style causal language model with:
+
+- 28 layers
+- hidden size 1536
+- 12 attention heads
+- 2 key-value heads
+- vocabulary size 151,936
+
+The training setup targets:
+
+- instruction following
+- structured response generation
+- distilled reasoning-flavored outputs
+
+rather than pure base-model continuation pretraining.
+
+---
+
+# Training objective
+
+The goal of v0.3 was to improve the model through offline SFT distillation from a synthetic/teacher-style dataset while preserving the usability and compact size of the 1.5B-class base model.
+
+The dataset examples are preformatted as ChatML conversations and frequently instruct the assistant to reason in a `<think>` block before producing a final `<answer>` block.
+
+Because of that training distribution, the model may naturally produce more structured, tutor-like, stepwise outputs than the earlier checkpoint depending on the prompt style.
+
+---
+
+# Base model
+
+- **Base model:** `abhinav0231/Lily-1.5b-v0.1`
+- **Final merged model repo:** `abhinav0231/Lily-1.5b-v0.3`
+- **GGUF Repo** `abhinav0231/Lily-1.5b-v0.3-GGUF`
+---
+
+# Benchmarks
+
+Evaluation setup using `lm-evaluation-harness`, v0.3 achieved:
+
+
+![image](https://cdn-uploads.huggingface.co/production/uploads/6617f58b9e7b3dbb1407d934/c6KDfKHIiEPmcwZi5qbBe.png)
+
+---
+
+# Dataset
+
+The main training dataset is:
+
+`abhinav0231/Sarvam-105b-Distill-100k`
+
+using the `chatml` configuration, stored as a single `text` column of preformatted conversations.
+
+The final training notebook loaded:
+
+- 91,457 training examples
+- 1,908 validation examples
+
+A separate sanity-check pass over the dataset family showed a very similar distribution, including:
+
+- 92,040 training examples
+- 1,917 validation examples
+- 1,918 test examples
+
+confirming the same overall ChatML reasoning-style format.
+
+---
+
+## Dataset style
+
+The dataset uses ChatML with:
+
+- `<|im_start|>`
+- `<|im_end|>`
+
+delimiters and includes a chat template in the tokenizer setup.
+
+Many examples use a system prompt that explicitly asks the assistant to think through the problem in a `<think>` block and then give the final response in an `<answer>` block.
+
+This means the model was not trained on plain raw instruction-response text alone; it was trained on a formatted conversational distribution with strong structural priors.
+
+---
+
+## Length characteristics
+
+A 5,000-sample sanity slice of the training set had:
+
+- mean length = 1640.72 tokens
+- p50 = 1219
+- p90 = 3221
+- p95 = 4096.15
+- p99 = 6883.35
+
+About:
+
+- 5.00% of sampled training examples
+- 4.33% of sampled validation examples
+
+exceeded 4096 tokens.
+
+These numbers matter because the training run used a 4096 token max sequence length, so the longest examples are subject to truncation or packing effects depending on preprocessing behavior.
+
+---
+
+# Training setup
+
+Training was run on a single NVIDIA A100-SXM4-40GB GPU in Modal, without:
+
+- DDP
+- `accelerate launch`
+- multi-process orchestration
+
+The environment used:
+
+- Unsloth 2026.5.2
+- TRL 0.22.2
+- PyTorch 2.8.0+cu129
+- CUDA 12.9
+- Triton 3.4.0
+- BF16 mixed precision
+
+Flash Attention 2 was auto-enabled by Unsloth because the A100 supports it.
+
+---
+
+## Core hyperparameters
+
+| Parameter | Value |
+|---|---|
+| Max sequence length | 4096 |
+| Num epochs | 2 |
+| Learning rate | 2e-5 |
+| Warmup steps | 100 |
+| Warmup ratio | 0.03 |
+| Batch size | 24 |
+| Gradient accumulation | 1 |
+| Effective batch size | 24 |
+| Seed | 42 |
+
+---
+
+## Optimization stack
+
+The model was loaded with QLoRA 4-bit weights during training, while the final merged checkpoint was saved in 16-bit merged form for deployment and inference use.
+
+The W&B config logged the optimizer as `adamw_8bit`, while the trainer config used fused AdamW (`adamw_torch_fused`) in the notebook training arguments.
+
+Sequence packing was enabled, dataset preprocessing used multiprocessing, and periodic evaluation/checkpoint saving was configured during the run.
+
+---
+
+# LoRA / PEFT details
+
+The fine-tuning used:
+
+- LoRA rank = 32
+- LoRA alpha = 64
+
+Target modules:
+
+- `q_proj`
+- `k_proj`
+- `v_proj`
+- `o_proj`
+- `gate_proj`
+- `up_proj`
+- `down_proj`
+
+The run reported approximately:
+
+- 36.9M trainable parameters
+
+which corresponded to around 2.34%–4.0% of total parameters depending on counting conventions.
+
+---
+
+# Hardware and runtime
+
+Training hardware:
+
+- NVIDIA A100-SXM4-40GB
+- ~42.4 GB VRAM exposed
+- Compute capability 8.0
+- BF16 support
+- Flash Attention 2 support
+
+The run specifically targeted A100-native BF16 and Flash Attention 2 optimizations.
+
+Total training runtime was approximately:
+
+- 5 hours 14 minutes
+
+---
+
+# Checkpointing and merge
+
+Intermediate checkpoints were pushed to:
+
+`abhinav0231/Lily-1.5b-distill-v3-checkpoints`
+
+during training.
+
+The workflow included auto-resume logic from the latest Hugging Face checkpoint.
+
+After training, the LoRA adapter was merged back into the base model in BF16/16-bit form and pushed as:
+
+`abhinav0231/Lily-1.5b-v0.3`
+
+The notebook also included GGUF export paths for quantized deployment variants.
+
+---
+
+# Training logs
+
+The trainer log reported:
+
+- 33,297 packed training examples
+- 2 epochs
+- 2,776 optimization steps
+
+Validation loss decreased from:
+
+- 9.100862 at step 500
+to
+- 8.973075 at step 2500
+
+These values should be interpreted as internal training diagnostics rather than direct end-user quality metrics.
+
+---
+
+# Intended use
+
+This model is intended for:
+
+- instruction-following chat experiments
+- structured answer generation
+- research on distilled reasoning-style outputs
+- lightweight local or hosted inference in the 1.5B parameter class
+
+It is especially suited to prompts where:
+
+- a user asks for explanations or breakdowns
+- the desired answer format is structured
+- the prompt resembles the ChatML style used during training
+
+---
+
+# Prompting notes
+
+Because the training data is ChatML-formatted, best results usually come from chat-style prompting rather than plain raw completion prompting.
+
+The model may respond in a more verbose tutor-like style because many training prompts encouraged detailed reasoning followed by a final answer.
+
+If a cleaner direct-answer style is preferred, using a concise system prompt and explicitly requesting short outputs can help steer generation.
+
+---
+
+# Limitations
+
+This model was trained on synthetic/distilled instruction data rather than broad raw web-scale pretraining data.
+
+As a result:
+
+- outputs may reflect teacher-style formatting biases
+- responses may become over-structured
+- reasoning markup may occasionally appear in generations
+
+The dataset sanity checks also flagged formatting irregularities in sampled rows, including repeated markers and malformed counts, so downstream behavior may inherit some formatting artifacts from the source corpus.
+
+---
+
+# Safety
+
+This model is not designed for fully autonomous use in high-stakes domains such as:
+
+- legal
+- medical
+- financial
+- safety-critical systems
+
+Outputs can still be:
+
+- incorrect
+- incomplete
+- overconfident
+
+Human review is recommended for consequential use cases.
+
+
+---
+
+# Usage
+
+## Transformers
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = "abhinav0231/Lily-1.5b-v0.3"
+
+tokenizer = AutoTokenizer.from_pretrained(
+    model_id,
+    trust_remote_code=True,
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    trust_remote_code=True,
+    device_map="auto",
+)
+
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": "Explain overfitting in simple terms."},
+]
+
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+)
+
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=512,
+        temperature=0.7,
+        do_sample=True,
+    )
+
+print(tokenizer.decode(outputs, skip_special_tokens=True))
+```
+
+### Suggested prompting
+
+For best results:
+- use chat-style prompts,
+- keep instructions explicit,
+- specify desired format,
+- request concise output if you do not want long reasoning-style responses.
+
+## Provenance
+
+- **Base model:** `abhinav0231/Lily-1.5b-v0.1`
+- **Training dataset:** `abhinav0231/Sarvam-105b-Distill-100k` (`chatml`)
+- **Training framework:** Unsloth + TRL
+- **Hardware:** 1x NVIDIA A100-SXM4-40GB
+- **Final merged repo:** `abhinav0231/Lily-1.5b-v0.3`
+
+## Acknowledgements
+
+This model was trained with Unsloth, Hugging Face Transformers, TRL, PEFT/LoRA-style fine-tuning, and W&B logging in a Modal-hosted workflow.
+
+This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

added_tokens.json

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+  "<tool_call>": 151657,
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+}

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": null,
+    "torch_dtype": "bfloat16",
+    "eos_token_id": 151645,
+    "hidden_act": "silu",
+    "hidden_size": 1536,
+    "initializer_range": 0.02,
+    "intermediate_size": 8960,
+    "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": 21,
+    "model_type": "qwen2",
+    "num_attention_heads": 12,
+    "num_hidden_layers": 28,
+    "num_key_value_heads": 2,
+    "pad_token_id": 151665,
+    "rms_norm_eps": 1e-06,
+    "rope_parameters": {
+        "rope_theta": 1000000.0,
+        "rope_type": "default"
+    },
+    "sliding_window": null,
+    "tie_word_embeddings": true,
+    "unsloth_fixed": true,
+    "unsloth_version": "2026.5.2",
+    "use_cache": true,
+    "use_sliding_window": false,
+    "vocab_size": 151936
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "eos_token_id": 151645,
+  "max_length": 32768,
+  "pad_token_id": 151665,
+  "transformers_version": "5.5.0",
+  "use_cache": true
+}

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special_tokens_map.json

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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<<|PAD_TOKEN|>>"
+}

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tokenizer_config.json

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+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "is_local": false,
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+  "pad_token": "<|PAD_TOKEN|>",
+  "padding_side": "right",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
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+    },
+    "151656": {
+      "content": "<|video_pad|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "151657": {
+      "content": "<tool_call>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "151658": {
+      "content": "</tool_call>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "151659": {
+      "content": "<|fim_prefix|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "151660": {
+      "content": "<|fim_middle|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "151661": {
+      "content": "<|fim_suffix|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "151662": {
+      "content": "<|fim_pad|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "151665": {
+      "content": "<|PAD_TOKEN|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    }
+  },
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n    {%- endif %}\n    {{- \"\\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>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\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\" }}\n{%- else %}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n"
+}