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Model: SeeYangZhi/Llama-3.2-1B-Sarcasm-Rewriter-Context
Source: Original Platform

.gitattributes

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README.md

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+---
+license: llama3.2
+base_model: meta-llama/Llama-3.2-1B-Instruct
+tags:
+- text-generation
+- style-transfer
+- sarcasm
+- llama
+language:
+- en
+pipeline_tag: text-generation
+---
+
+# Llama-3.2-1B-Sarcasm-Rewriter-Context
+
+A LoRA fine-tuned [Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) that rewrites sarcastic news headlines as neutral, factual equivalents. Trained with **article body context** in the prompt during supervised fine-tuning, producing stronger sarcasm comprehension than headline-only training.
+
+Built by CS4248 Team 14 (NUS, AY2025/26 Semester 2) as part of a sarcasm style transfer research project.
+
+## Why this model
+
+Compared to the sibling [`Llama-3.2-1B-Sarcasm-Rewriter`](https://huggingface.co/SeeYangZhi/Llama-3.2-1B-Sarcasm-Rewriter) (headline-only training), this context-enhanced variant:
+
+- Lower perplexity (318 vs 378)
+- Higher LLM-judged sarcasm removal score (4.96/5 vs 4.74/5)
+- **Better meaning preservation** (4.32/5 vs 3.80/5) — the largest improvement
+- Same near-perfect fluency (4.98/5)
+
+The training targets were generated by an LLM annotator that had access to the full article body, producing deeper rewrites than headline-only targets. The model learned to mimic these more faithful rewrites.
+
+## Task
+
+**Input**: A sarcastic news headline
+**Output**: A non-sarcastic rewrite
+
+```
+Input:  "Inconsiderate Wife Leaves Bathroom A Total Mess After Home Birth"
+Output: "Mother of Two Gives Birth at Home"
+```
+
+## Training
+
+- **Base model**: [`meta-llama/Llama-3.2-1B-Instruct`](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) (1.24B params)
+- **Method**: LoRA (r=16, α=32, dropout=0.05) targeting `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`
+- **Trainable parameters**: ~11.3M (0.9% of base)
+- **Dataset**: 6,463 sarcastic→non-sarcastic headline pairs where article bodies were available. Targets generated by StepFun Step-3.5 Flash (LLM annotator with article body access), cross-validated by Nemotron. Split: `sar_to_non_context_enhanced` with body filter applied.
+- **Prompt format (training)**: system prompt + user turn containing both the sarcastic headline AND the full article body as context
+- **Loss**: Computed only on the assistant response tokens (target headline), not on the prompt
+- **Training setup**: 3 epochs on H200 GPU, LR 2e-4 cosine, batch 4 × grad_accum 4, bfloat16, gradient checkpointing
+- **Best checkpoint**: Epoch 1 (eval_loss 1.492)
+
+After training, the LoRA adapter was merged into the base weights via `merge_and_unload()`.
+
+## Usage — Recommended (headline-only prompt)
+
+Even though the model was trained with article bodies, **inference-time evaluation showed the model performs best with headline-only prompts**. Feeding article bodies at inference introduces hallucination from article content. Use this configuration in production:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "SeeYangZhi/Llama-3.2-1B-Sarcasm-Rewriter-Context"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+
+messages = [
+    {
+        "role": "system",
+        "content": (
+            "You are a writing assistant. Rewrite sarcastic news headlines as neutral, "
+            "factual equivalents that preserve the core meaning without irony or mockery. "
+            "Respond with only the rewritten headline, no explanation."
+        ),
+    },
+    {
+        "role": "user",
+        "content": (
+            "Rewrite this sarcastic headline as a neutral, non-sarcastic news headline:\n\n"
+            "inconsiderate wife leaves bathroom a total mess after home birth"
+        ),
+    },
+]
+
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=128, do_sample=False)
+print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
+```
+
+## Usage — Alternative (with article body, matches training distribution)
+
+If you have the source article body available, you can pass it in the prompt. Note that evaluation showed this mode produces slightly worse outputs than headline-only due to body-distractor hallucination, so it is not recommended:
+
+```python
+user_content = (
+    "Rewrite this sarcastic headline as a neutral, non-sarcastic news headline.\n\n"
+    f"Headline: {sarcastic_headline}\n\n"
+    f"Article context:\n{article_body}"
+)
+```
+
+## Evaluation
+
+Compared against 14 other models (BART variants, T5 variants, ablations, previous LLaMA) on a 2,857-sample held-out test split with 7 metrics. Key results vs previous headline-only variant:
+
+| Metric | Llama-context (this model) | Llama (previous) |
+|---|---|---|
+| Flip rate (classifier) | 22.5% | 21.9% |
+| Semantic similarity | 0.679 | 0.656 |
+| Perplexity (GPT-2) | **318** | 378 |
+| LLM sarcasm removed | **4.96/5** | 4.74/5 |
+| LLM meaning preserved | **4.32/5** | 3.80/5 |
+| LLM fluency | 4.98/5 | 4.98/5 |
+
+Full per-metric numbers are published alongside the project webapp.
+
+## License
+
+This model is released under the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE). The model name starts with "Llama-" as required by Meta's terms. Built with Llama.
+
+## Citation
+
+If you use this model, please cite the underlying Llama 3.2 release and the NHDSD dataset.

chat_template.jinja

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+{{- bos_token }}
+{%- if custom_tools is defined %}
+    {%- set tools = custom_tools %}
+{%- endif %}
+{%- if not tools_in_user_message is defined %}
+    {%- set tools_in_user_message = true %}
+{%- endif %}
+{%- if not date_string is defined %}
+    {%- if strftime_now is defined %}
+        {%- set date_string = strftime_now("%d %b %Y") %}
+    {%- else %}
+        {%- set date_string = "26 Jul 2024" %}
+    {%- endif %}
+{%- endif %}
+{%- if not tools is defined %}
+    {%- set tools = none %}
+{%- endif %}
+
+{#- This block extracts the system message, so we can slot it into the right place. #}
+{%- if messages[0]['role'] == 'system' %}
+    {%- set system_message = messages[0]['content']|trim %}
+    {%- set messages = messages[1:] %}
+{%- else %}
+    {%- set system_message = "" %}
+{%- endif %}
+
+{#- System message #}
+{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
+{%- if tools is not none %}
+    {{- "Environment: ipython\n" }}
+{%- endif %}
+{{- "Cutting Knowledge Date: December 2023\n" }}
+{{- "Today Date: " + date_string + "\n\n" }}
+{%- if tools is not none and not tools_in_user_message %}
+    {{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+{%- endif %}
+{{- system_message }}
+{{- "<|eot_id|>" }}
+
+{#- Custom tools are passed in a user message with some extra guidance #}
+{%- if tools_in_user_message and not tools is none %}
+    {#- Extract the first user message so we can plug it in here #}
+    {%- if messages | length != 0 %}
+        {%- set first_user_message = messages[0]['content']|trim %}
+        {%- set messages = messages[1:] %}
+    {%- else %}
+        {{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
+{%- endif %}
+    {{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
+    {{- "Given the following functions, please respond with a JSON for a function call " }}
+    {{- "with its proper arguments that best answers the given prompt.\n\n" }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+    {{- first_user_message + "<|eot_id|>"}}
+{%- endif %}
+
+{%- for message in messages %}
+    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
+        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
+    {%- elif 'tool_calls' in message %}
+        {%- if not message.tool_calls|length == 1 %}
+            {{- raise_exception("This model only supports single tool-calls at once!") }}
+        {%- endif %}
+        {%- set tool_call = message.tool_calls[0].function %}
+        {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
+        {{- '{"name": "' + tool_call.name + '", ' }}
+        {{- '"parameters": ' }}
+        {{- tool_call.arguments | tojson }}
+        {{- "}" }}
+        {{- "<|eot_id|>" }}
+    {%- elif message.role == "tool" or message.role == "ipython" %}
+        {{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
+        {%- if message.content is mapping or message.content is iterable %}
+            {{- message.content | tojson }}
+        {%- else %}
+            {{- message.content }}
+        {%- endif %}
+        {{- "<|eot_id|>" }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "dtype": "bfloat16",
+  "eos_token_id": 128009,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 16,
+  "num_key_value_heads": 8,
+  "pad_token_id": 128009,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_parameters": {
+    "factor": 32.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_theta": 500000.0,
+    "rope_type": "llama3"
+  },
+  "tie_word_embeddings": true,
+  "transformers_version": "5.5.0",
+  "use_cache": false,
+  "vocab_size": 128256
+}

generation_config.json

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+{
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
+    128009,
+    128001,
+    128008,
+    128009
+  ],
+  "pad_token_id": 128009,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "5.5.0"
+}

model.safetensors

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+size 2471645608

tokenizer.json

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tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "bos_token": "<|begin_of_text|>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|eot_id|>",
+  "is_local": false,
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "pad_token": "<|eot_id|>",
+  "tokenizer_class": "TokenizersBackend"
+}