can you help me build an Ollama modelfile in vs code for a custom llm model? here is the doc: ""Ollama Model File


A model file is the blueprint to create and share models with Ollama.

Table of Contents
Format
Examples
Instructions
FROM (Required)
Build from llama2
Build from a bin file
PARAMETER
Valid Parameters and Values
TEMPLATE
Template Variables
SYSTEM
ADAPTER
LICENSE
MESSAGE
Notes
Format
The format of the Modelfile:

# comment
INSTRUCTION arguments
Instruction	Description
FROM (required)	Defines the base model to use.
PARAMETER	Sets the parameters for how Ollama will run the model.
TEMPLATE	The full prompt template to be sent to the model.
SYSTEM	Specifies the system message that will be set in the template.
ADAPTER	Defines the (Q)LoRA adapters to apply to the model.
LICENSE	Specifies the legal license.
MESSAGE	Specify message history.
Examples
Basic Modelfile
An example of a Modelfile creating a mario blueprint:

FROM llama2
# sets the temperature to 1 [higher is more creative, lower is more coherent]
PARAMETER temperature 1
# sets the context window size to 4096, this controls how many tokens the LLM can use as context to generate the next token
PARAMETER num_ctx 4096

# sets a custom system message to specify the behavior of the chat assistant
SYSTEM You are Mario from super mario bros, acting as an assistant.
To use this:

Save it as a file (e.g. Modelfile)
ollama create choose-a-model-name -f <location of the file e.g. ./Modelfile>'
ollama run choose-a-model-name
Start using the model!
More examples are available in the examples directory.

Modelfiles in ollama.com/library
There are two ways to view Modelfiles underlying the models in ollama.com/library:

Option 1: view a details page from a model's tags page:

Go to a particular model's tags (e.g. https://ollama.com/library/llama2/tags)
Click on a tag (e.g. https://ollama.com/library/llama2:13b)
Scroll down to "Layers"
Note: if the FROM instruction is not present, it means the model was created from a local file
Option 2: use ollama show to print the Modelfile for any local models like so:

> ollama show --modelfile llama2:13b
# Modelfile generated by "ollama show"
# To build a new Modelfile based on this one, replace the FROM line with:
# FROM llama2:13b

FROM /root/.ollama/models/blobs/sha256:123abc
TEMPLATE """
 {{ if .System }}
{{ .System }}


{{ end }}{{ .Prompt }} 
 """
SYSTEM """"""
PARAMETER stop 

PARAMETER stop 

PARAMETER stop 

PARAMETER stop 

Instructions
FROM (Required)
The FROM instruction defines the base model to use when creating a model.

FROM <model name>:<tag>
Build from llama2
FROM llama2
A list of available base models: https://github.com/jmorganca/ollama#model-library

Build from a bin file
FROM ./ollama-model.bin
This bin file location should be specified as an absolute path or relative to the Modelfile location.

PARAMETER
The PARAMETER instruction defines a parameter that can be set when the model is run.

PARAMETER <parameter> <parametervalue>
Valid Parameters and Values
Parameter	Description	Value Type	Example Usage
mirostat	Enable Mirostat sampling for controlling perplexity. (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)	int	mirostat 0
mirostat_eta	Influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher learning rate will make the algorithm more responsive. (Default: 0.1)	float	mirostat_eta 0.1
mirostat_tau	Controls the balance between coherence and diversity of the output. A lower value will result in more focused and coherent text. (Default: 5.0)	float	mirostat_tau 5.0
num_ctx	Sets the size of the context window used to generate the next token. (Default: 2048)	int	num_ctx 4096
num_gqa	The number of GQA groups in the transformer layer. Required for some models, for example it is 8 for llama2:70b	int	num_gqa 1
num_gpu	The number of layers to send to the GPU(s). On macOS it defaults to 1 to enable metal support, 0 to disable.	int	num_gpu 50
num_thread	Sets the number of threads to use during computation. By default, Ollama will detect this for optimal performance. It is recommended to set this value to the number of physical CPU cores your system has (as opposed to the logical number of cores).	int	num_thread 8
repeat_last_n	Sets how far back for the model to look back to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)	int	repeat_last_n 64
repeat_penalty	Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. (Default: 1.1)	float	repeat_penalty 1.1
temperature	The temperature of the model. Increasing the temperature will make the model answer more creatively. (Default: 0.8)	float	temperature 0.7
seed	Sets the random number seed to use for generation. Setting this to a specific number will make the model generate the same text for the same prompt. (Default: 0)	int	seed 42
stop	Sets the stop sequences to use. When this pattern is encountered the LLM will stop generating text and return. Multiple stop patterns may be set by specifying multiple separate stop parameters in a modelfile.	string	stop "AI assistant:"
tfs_z	Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting. (default: 1)	float	tfs_z 1
num_predict	Maximum number of tokens to predict when generating text. (Default: 128, -1 = infinite generation, -2 = fill context)	int	num_predict 42
top_k	Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. (Default: 40)	int	top_k 40
top_p	Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. (Default: 0.9)	float	top_p 0.9
TEMPLATE
TEMPLATE of the full prompt template to be passed into the model. It may include (optionally) a system message, a user's message and the response from the model. Note: syntax may be model specific. Templates use Go template syntax.

Template Variables
Variable	Description
{{ .System }}	The system message used to specify custom behavior.
{{ .Prompt }}	The user prompt message.
{{ .Response }}	The response from the model. When generating a response, text after this variable is omitted.
TEMPLATE """{{ if .System }}
system
{{ .System }}

{{ end }}{{ if .Prompt }}
user
{{ .Prompt }}

{{ end }}
assistant
"""
SYSTEM
The SYSTEM instruction specifies the system message to be used in the template, if applicable.

SYSTEM """<system message>"""
ADAPTER
The ADAPTER instruction specifies the LoRA adapter to apply to the base model. The value of this instruction should be an absolute path or a path relative to the Modelfile and the file must be in a GGML file format. The adapter should be tuned from the base model otherwise the behaviour is undefined.

ADAPTER ./ollama-lora.bin
LICENSE
The LICENSE instruction allows you to specify the legal license under which the model used with this Modelfile is shared or distributed.

LICENSE """
<license text>
"""
MESSAGE
The MESSAGE instruction allows you to specify a message history for the model to use when responding. Use multiple iterations of the MESSAGE command to build up a conversation which will guide the model to answer in a similar way.

MESSAGE <role> <message>
Valid roles
Role	Description
system	Alternate way of providing the SYSTEM message for the model.
user	An example message of what the user could have asked.
assistant	An example message of how the model should respond.
Example conversation
MESSAGE user Is Toronto in Canada?
MESSAGE assistant yes
MESSAGE user Is Sacramento in Canada?
MESSAGE assistant no
MESSAGE user Is Ontario in Canada?
MESSAGE assistant yes
Notes
the Modelfile is not case sensitive. In the examples, uppercase instructions are used to make it easier to distinguish it from arguments.
Instructions can be in any order. In the examples, the FROM instruction is first to keep it easily readable.""

here is the information about that contains the prompt format: ""To prompt this model, ensure that the format is consistent with that of TuluV2. For example, a prompt should be formatted as follows, where 
 corresponds to the human's role and 
 corresponds to the LLM's role. The human should speak first:




Hi! I'm looking for a cake recipe.


What kind of cake?


Chocolate cake.



Note that a beginning-of-sequence (BOS) token is automatically added at tokenization time and does not have to be added by you. No end-of-sequence (EOS) token is added to the prompt. You may also use our tokenizer's apply_chat_template if doing inference with chatml set or evaluating generations through non-local clients.""

the filepath of the model is "C:\Users\bower\.ollama\models\blobs\Contextual_KTO_Mistral_PairRM.q6_K.gguf"