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adapter_config.json

@@ -0,0 +1,46 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "AlignmentResearch/Llama-3.3-Tiny-Instruct-boolq",
+  "bias": "none",
+  "corda_config": null,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": false,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 256,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 128,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "model.layers.1.mlp.gate_proj",
+    "model.layers.1.mlp.down_proj",
+    "model.layers.0.self_attn.o_proj",
+    "model.layers.1.self_attn.v_proj",
+    "model.layers.1.self_attn.q_proj",
+    "model.layers.0.mlp.up_proj",
+    "model.layers.0.mlp.gate_proj",
+    "model.layers.0.mlp.down_proj",
+    "model.layers.1.self_attn.o_proj",
+    "model.layers.0.self_attn.v_proj",
+    "model.layers.1.self_attn.k_proj",
+    "model.layers.0.self_attn.k_proj",
+    "model.layers.1.mlp.up_proj",
+    "model.layers.0.self_attn.q_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_rslora": false
+}

adapter_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4e9e0027bbdf7ccaff18bbb895264e1610d96dd30b2b0398f1011146d515d7c7
+size 18098267

chat_template.jinja

@@ -0,0 +1,109 @@
+{{- 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 %}
+    {%- set date_string = "26 Jul 2024" %}
+{%- 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 + builtin tools #}
+{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
+{%- if builtin_tools is defined or tools is not none %}
+    {{- "Environment: ipython\n" }}
+{%- endif %}
+{%- if builtin_tools is defined %}
+    {{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "\n\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 %}
+        {%- if builtin_tools is defined and tool_call.name in builtin_tools %}
+            {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
+            {{- "<|python_tag|>" + tool_call.name + ".call(" }}
+            {%- for arg_name, arg_val in tool_call.arguments | items %}
+                {{- arg_name + '="' + arg_val + '"' }}
+                {%- if not loop.last %}
+                    {{- ", " }}
+                {%- endif %}
+                {%- endfor %}
+            {{- ")" }}
+        {%- else  %}
+            {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
+            {{- '{"name": "' + tool_call.name + '", ' }}
+            {{- '"parameters": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- "}" }}
+        {%- endif %}
+        {%- if builtin_tools is defined %}
+            {#- This means we're in ipython mode #}
+            {{- "<|eom_id|>" }}
+        {%- else %}
+            {{- "<|eot_id|>" }}
+        {%- endif %}
+    {%- 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 %}

final/adapter_config.json

@@ -0,0 +1,46 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "AlignmentResearch/Llama-3.3-Tiny-Instruct-boolq",
+  "bias": "none",
+  "corda_config": null,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": false,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 256,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 128,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "model.layers.1.mlp.gate_proj",
+    "model.layers.1.mlp.down_proj",
+    "model.layers.0.self_attn.o_proj",
+    "model.layers.1.self_attn.v_proj",
+    "model.layers.1.self_attn.q_proj",
+    "model.layers.0.mlp.up_proj",
+    "model.layers.0.mlp.gate_proj",
+    "model.layers.0.mlp.down_proj",
+    "model.layers.1.self_attn.o_proj",
+    "model.layers.0.self_attn.v_proj",
+    "model.layers.1.self_attn.k_proj",
+    "model.layers.0.self_attn.k_proj",
+    "model.layers.1.mlp.up_proj",
+    "model.layers.0.self_attn.q_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_rslora": false
+}

final/adapter_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4e9e0027bbdf7ccaff18bbb895264e1610d96dd30b2b0398f1011146d515d7c7
+size 18098267

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

step_0/adapter_config.json

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+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "AlignmentResearch/Llama-3.3-Tiny-Instruct-boolq",
+  "bias": "none",
+  "corda_config": null,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": false,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 256,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 128,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "model.layers.1.mlp.gate_proj",
+    "model.layers.1.mlp.down_proj",
+    "model.layers.0.self_attn.o_proj",
+    "model.layers.1.self_attn.v_proj",
+    "model.layers.1.self_attn.q_proj",
+    "model.layers.0.mlp.up_proj",
+    "model.layers.0.mlp.gate_proj",
+    "model.layers.0.mlp.down_proj",
+    "model.layers.1.self_attn.o_proj",
+    "model.layers.0.self_attn.v_proj",
+    "model.layers.1.self_attn.k_proj",
+    "model.layers.0.self_attn.k_proj",
+    "model.layers.1.mlp.up_proj",
+    "model.layers.0.self_attn.q_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_rslora": false
+}

step_0/adapter_model.bin

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step_1000/adapter_config.json

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+{
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+  "base_model_name_or_path": "AlignmentResearch/Llama-3.3-Tiny-Instruct-boolq",
+  "bias": "none",
+  "corda_config": null,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": false,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 256,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 128,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
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+    "model.layers.1.mlp.down_proj",
+    "model.layers.0.self_attn.o_proj",
+    "model.layers.1.self_attn.v_proj",
+    "model.layers.1.self_attn.q_proj",
+    "model.layers.0.mlp.up_proj",
+    "model.layers.0.mlp.gate_proj",
+    "model.layers.0.mlp.down_proj",
+    "model.layers.1.self_attn.o_proj",
+    "model.layers.0.self_attn.v_proj",
+    "model.layers.1.self_attn.k_proj",
+    "model.layers.0.self_attn.k_proj",
+    "model.layers.1.mlp.up_proj",
+    "model.layers.0.self_attn.q_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_rslora": false
+}

step_1000/adapter_model.bin

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step_1008/adapter_config.json

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+{
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+  "base_model_name_or_path": "AlignmentResearch/Llama-3.3-Tiny-Instruct-boolq",
+  "bias": "none",
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+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": false,
+  "init_lora_weights": true,
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+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 256,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 128,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
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+    "model.layers.1.mlp.down_proj",
+    "model.layers.0.self_attn.o_proj",
+    "model.layers.1.self_attn.v_proj",
+    "model.layers.1.self_attn.q_proj",
+    "model.layers.0.mlp.up_proj",
+    "model.layers.0.mlp.gate_proj",
+    "model.layers.0.mlp.down_proj",
+    "model.layers.1.self_attn.o_proj",
+    "model.layers.0.self_attn.v_proj",
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+    "model.layers.1.mlp.up_proj",
+    "model.layers.0.self_attn.q_proj"
+  ],
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+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_rslora": false
+}

step_1008/adapter_model.bin

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step_1016/adapter_config.json

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+  "base_model_name_or_path": "AlignmentResearch/Llama-3.3-Tiny-Instruct-boolq",
+  "bias": "none",
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+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": false,
+  "init_lora_weights": true,
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+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 256,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 128,
+  "rank_pattern": {},
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+  "target_modules": [
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+  "use_dora": false,
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+}

step_1016/adapter_model.bin

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step_1024/adapter_config.json

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+{
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+  "base_model_name_or_path": "AlignmentResearch/Llama-3.3-Tiny-Instruct-boolq",
+  "bias": "none",
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+  "lora_bias": false,
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+  "megatron_core": "megatron.core",
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+  "peft_type": "LORA",
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+  "rank_pattern": {},
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+  "use_dora": false,
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+}

step_1024/adapter_model.bin

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step_1032/adapter_config.json

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+{
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+  "base_model_name_or_path": "AlignmentResearch/Llama-3.3-Tiny-Instruct-boolq",
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+  "megatron_core": "megatron.core",
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+}

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