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Model: SeeYangZhi/Llama-3.2-1B-Sarcasm-Rewriter Source: Original Platform
2026-06-14 17:54:15 +08:00
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--- a/README.md
+++ b/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
+
+A 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 while preserving the underlying meaning.
+
+Built by CS4248 Team 14 (NUS, AY2025/26 Semester 2) as part of a sarcasm style transfer research project.
+
+## Task
+
+**Input**: A sarcastic news headline (e.g. *"Area Man Passionate Defender Of What He Imagines Constitution To Be"*)
+**Output**: A non-sarcastic rewrite (e.g. *"A man strongly defends his interpretation of the Constitution"*)
+
+## Training
+
+- **Base model**: `meta-llama/Llama-3.2-1B-Instruct`
+- **Method**: LoRA (r=16, alpha=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**: 8,258 sarcastic->non-sarcastic headline pairs derived from NHDSD (News Headlines Dataset for Sarcasm Detection). Non-sarcastic targets were generated by an LLM annotator (StepFun Step-3.5 Flash) informed by the original article body where available, with cross-validation by Nemotron. Split: `sar_to_non_context_enhanced`.
+- **Loss**: Computed only on assistant response tokens (not on the prompt)
+- After training, the LoRA adapter was merged into the base weights via `merge_and_unload()`.
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "SeeYangZhi/Llama-3.2-1B-Sarcasm-Rewriter"
+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\narea man passionate defender of what he imagines constitution to be",
+    },
+]
+
+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))
+```
+
+## Evaluation
+
+Evaluated on a 2,857-sample held-out test split against 13 other models (BART variants, T5 baselines, ablation studies). Metrics:
+
+| Metric | Description | Direction |
+|---|---|---|
+| Hard Flip Rate | % of samples where sarcasm was removed (classifier-detected) | higher ↑ |
+| Flip Delta | Mean change in irony score | higher ↑ |
+| Semantic Similarity | all-MiniLM-L6-v2 cosine between input and output | higher ↑ |
+| BLEU vs input | Lower = more genuine rewriting | lower ↓ |
+| Perplexity (GPT-2) | Fluency | lower ↓ |
+| Edit Distance (norm) | Proportion of words changed | higher ↑ |
+| Paraphrase Score | Paraphrase detection (low = real rewriting) | lower ↓ |
+
+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. Project writeup available upon request.
--- a/chat_template.jinja
+++ b/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 %}
--- a/config.json
+++ b/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
+}
--- a/generation_config.json
+++ b/generation_config.json
@@ -0,0 +1,14 @@
+{
+  "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"
+}
--- a/llmao-llama-1b-sarcasm.gguf
+++ b/llmao-llama-1b-sarcasm.gguf
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+oid sha256:0b526c93ddaf974eb47414964683de8f1484a19f16100830b62ba4c6500e99da
+size 2479595168
--- a/model.safetensors
+++ b/model.safetensors
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+oid sha256:a0dc6358ad0b7557eccce78a3cd2f8e698c11855ac1e366e432f387821af22ae
+size 2471645608
--- a/tokenizer.json
+++ b/tokenizer.json
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+oid sha256:13574c77ca1d572525cfa7caac46cee99309100524dad568a7ef85ae383df39f
+size 17210018
--- a/tokenizer_config.json
+++ b/tokenizer_config.json
@@ -0,0 +1,14 @@
+{
+  "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"
+}